Year: 2020

Abstract:
As neural network classifiers are deployed in real-world applications, it is crucial that their predictions are not just accurate, but trustworthy as well. One practical solution is to assign confidence scores to each prediction, then filter out lowconfidence predictions. However, existing confidence metrics are not yet sufficiently reliable for this role. This paper presents a new framework that produces more reliable confidence scores for detecting misclassification errors. This framework, RED, calibrates the classifier’s inherent confidence indicators and estimates uncertainty of the calibrated confidence scores using Gaussian Processes. Empirical comparisons with other confidence estimation methods on 125 UCI datasets demonstrate that this approach is effective. An experiment on a vision task with a large deep learning architecture further confirms that the method can scale up, and a case study involving out-of-distribution and adversarial samples shows potential of the proposed method to improve robustness of neural network classifiers more broadly in the future.

heading: 1 INTRODUCTION
text: Classifiers based on Neural Networks (NNs) are widely deployed in many real-world applications (LeCun et al., 2015; Anjos et al., 2015; Alghoul et al., 2018; Shahid et al., 2019). Although good prediction accuracies are achieved, lack of safety guarantees becomes a severe issue when NNs are applied to safety-critical domains, e.g., healthcare (Selişteanu et al., 2018; Gupta et al., 2007; Shahid et al., 2019), finance (Dixon et al., 2017), self-driving (Janai et al., 2017; Hecker et al., 2018), etc.
One way to estimate trustworthiness of a classifier prediction is to use its inherent confidence-related score, e.g., the maximum class probability (Hendrycks & Gimpel, 2017), entropy of the softmax outputs (Williams & Renals, 1997), or difference between the highest and second highest activation outputs (Monteith & Martinez, 2010). However, these scores are unreliable and may even be misleading: high-confidence but erroneous predictions are frequently observed (Provost et al., 1998; Guo et al., 2017; Nguyen et al., 2015; Goodfellow et al., 2014; Amodei et al., 2016). In a practical setting, it is beneficial to have a detector that can raise a red flag whenever the predictions are suspicious. A human observer can then evaluate such predictions, making the classification system safer.
In order to construct such a detector, quantitative metrics for measuring predictive reliability under different circumstances are first developed, and a warning threshold then be set based on users’ preferred precision-recall tradeoff. Existing such methods can be categorized into three types based on their focus: error detection, which aims to detect the natural misclassifications made by the classifier (Hendrycks & Gimpel, 2017; Jiang et al., 2018; Corbière et al., 2019); out-of-distribution (OOD) detection, which reports samples that are from different distributions compared to training data (Liang et al., 2018; Lee et al., 2018a; Devries & Taylor, 2018); and adversarial sample detection, which filters out samples from adversarial attacks (Lee et al., 2018b; Wang et al., 2019; Aigrain & Detyniecki, 2019).
Among these categories, error detection, also called misclassification detection (Jiang et al., 2018) or failure prediction (Corbière et al., 2019), is the most challenging (Aigrain & Detyniecki, 2019) and underexplored. For instance, Hendrycks & Gimpel (2017) defined a baseline based on maximum class probability after softmax layer. Although the baseline performs reasonably well in most testing cases, reduced efficacy in some scenaria indicates room for improvement (Hendrycks & Gimpel,
2017). Jiang et al. (2018) proposed Trust Score, which measures the similarity between the original classifier and a modified nearest-neighbor classifier. The main limitation of this method is scalability: the Trust Score may provide no or negative improvement over the baseline for high-dimensional data. ConfidNet (Corbière et al., 2019) builds a separate NN model to learn the true class probablity, i.e. softmax probability for the ground-truth class. However, ConfidNet itself is a standard NN, so its confidence scores may be unreliable or misleading: A random input may generate a random confidence score, and ConfidNet do not provide any information regarding uncertainty of these confidence scores. Moreover, none of these methods can differentiate natural classifier errors from risks caused by OOD or adversarial samples; if a detector could do that, it would be easier for practitioners to fix the problem, e.g., by retraining the original classifier or applying better preprocessing techniques to filter out OOD or adversarial data.
To meet these challenges, a new framework is developed in this paper for error detection in NN classifiers. The main idea is to utilize RIO (Residual, Input, Output; Qiu et al., 2020), a regression model based on Gaussian Processes, on top of the original NN classifier. Whereas the original RIO is limited to regression problems, the proposed approach extends it to misclassification detection by modifying its components. The new framework not only produces a calibrated confidence score based on the original maximum class probability, but also provides a quantitative uncertainty estimation of that score. Errors can therefore be detected more reliably. Note that the proposed method does not change the prediction accuracy of the original classification model. Instead, it provides a quantitative metric that makes it possible to detect misclassification errors. This framework, referred to as RED (RIO for Error Detection), is compared empirically to existing approaches on 125 UCI datasets and on a large-scale deep learning architecture. The results demonstrate that the approach is effective and robust. A further case study with OOD and adversarial samples shows the potential of using RED to diagnose the sources of mistakes as well, thereby leading to a possible comprehensive approach for improving trustworthiness of neural network classifiers in the future.

heading: 2 RELATED WORK
text: In the past two decades, a large volume of work was devoted to calibrating the confidence scores returned by classifiers. Early works include Platt Scaling (Platt, 1999; Niculescu-Mizil & Caruana, 2005), histogram binning (Zadrozny & Elkan, 2001), isotonic regression (Zadrozny & Elkan, 2002), with recent extensions like Temperature Scaling (Guo et al., 2017), Dirichlet calibration (Kull et al., 2019), and distance-based learning from errors (Xing et al., 2020). These methods focus on reducing the difference between reported class probability and true accuracy, and generally the rankings of samples are preserved after calibration. As a result, the separability between correct and incorrect predictions is not improved. In contrast, RED aims at deriving a score that can differentiate incorrect predictions from correct ones better.
A related direction of work is the development of classifiers with rejection/abstention option. These approaches either introduce new training pipelines/loss functions (Bartlett & Wegkamp, 2008; Yuan & Wegkamp, 2010; Cortes et al., 2016), or define mechanisms for learning rejection thresholds under certain risk levels (Dubuisson & Masson, 1993; Santos-Pereira & Pires, 2005; Chow, 2006; Geifman & El-Yaniv, 2017). In contrast to these methods, RED assumes an existing pretrained NN classifier, and provides an additional metric for detecting potential errors made by this classifier, without specifying a rejection threshold.
Designing metrics for detecting potential risks in NN classifiers has also become popular recently. While most approaches focus on detecting OOD (Liang et al., 2018; Lee et al., 2018a; Devries & Taylor, 2018) or adversarial examples (Lee et al., 2018b; Wang et al., 2019; Aigrain & Detyniecki, 2019), work on detecting natural errors, i.e., regular misclassifications not caused by external sources, is more limited. Ortega (1995) and Koppel & Engelson (1996) conducted early work in predicting whether a classifier is going to make mistakes, and Seewald & Fürnkranz (2001) built a meta-grading classifier based on similar ideas. However, these early works did not consider NN classifiers. More recently, Hendrycks & Gimpel (2017) and Haldimann et al. (2019) demonstrated that raw maximum class probability is an effective baseline in error detection, although its performance was reduced in some scenaria.
More elaborate techniques for error detection have also been developed recently. Mandelbaum & Weinshall (2017) proposed a confidence score based on the data embedding derived from the penul-
timate layer of a NN. However, their approach requires modifying the training procedure in order to achieve effective embeddings. Jiang et al. (2018) introduced Trust Score to measure the similarity between a base classifier and a modified nearest-neighbor classifier. Trust Score outperforms the maximum class probability baseline in many cases, but negative improvement over baseline can be observed in high-dimensional problems, implying poor scalability of local distance computations. ConfidNet (Corbière et al., 2019) learns to predict the class probability of true class with another NN, while Introspection-Net (Aigrain & Detyniecki, 2019) utilizes the logit activations of the original NN classifier to predict its correctness. Since both models themselves are standard NNs, the confidence scores returned by them may be arbitrarily high without any uncertainty information. Moreover, existing approaches for error detection cannot differentiate natural misclassification error from OOD or adversarial samples, making it difficult to diagnose the sources of risks. In contrast, RED explicitly reports its uncertainty about the estimated confidence score, providing more reliable error detection. The uncertainty information returned by RED may also be helpful in clarifying the cause of classifier mistakes, as will be demonstrated in this paper.

heading: 3 METHODOLOGY
text: This section gives the general problem statement, introduces the basic idea of original RIO, on which RED is built, and describes the technical details of RED.

heading: 3.1 PROBLEM STATEMENT
text: Consider a training dataset D = (X ,y) = {(xi, yi)}Ni=1, and a pretrained NN classifier that outputs a predicted label ŷi and class probabilities for each class σi = [p̂i,1, p̂i,2, . . . , p̂i,K ] given xi, where N is the total number of training points and K is the total number of classes. The problem is to develop a metric that can serve as a quantitative indicator for detecting natural misclassification errors made by the pretrained NN classifier.

heading: 3.2 RIO
text: The original RIO (Qiu et al., 2020) was developed to quantify point-prediction uncertainty in regression models. More specifically, RIO fits a Gaussian Process (GP) to predict the residuals, i.e. the differences between ground-truth and original model predictions. It utilizes an I/O kernel, i.e. a composite of an input kernel and an output kernel, thus taking into account both inputs and outputs of the original regression model. As a result, it measures the covariances between data points in both the original feature space and the original model output space. For each new data point, a trained RIO model takes the original input and output of the base regression model, and predicts a distribution of the residual, which can be added back to the original model prediction to obtain both a calibrated prediction and the corresponding predictive uncertainty.
In the original RIO work, SVGP (Hensman et al., 2013; 2015) was used as an approximate GP to improve the scalability of the approach. Both empirical results and theoretical analysis showed that RIO is able to consistently improve the prediction accuracy of base model as well as provide reliable uncertainty estimation. Moreover, RIO can be directly applied on top of any pre-trained models without retraining or modification. It therefore forms a promising foundation for improving reliability of error detection metrics as well.

heading: 3.3 RIO FOR ERROR DETECTION (RED)
text: Although RIO performs robustly in a wide variety of regression problems, it cannot be directly applied to classification models. A new framework, namely RED, is proposed to utilize RIO for error detection in classification domains.
Building on the fact that the original maximum class probability is a strong baseline for error detection (Hendrycks & Gimpel, 2017; Haldimann et al., 2019), the main idea of RED is to derive a more reliable confidence score by stacking RIO on top of the original maximum class probability. Since RIO was designed for single-output regression problems, it contains an output kernel only for scalar outputs. In RED, this original output kernel is extended to multiple outputs, i.e. to vector outputs
Algorithm 1 RED Training and Deployment Procedures Require:
(X ,y) = {(xi, yi)}Ni=1: training data ŷ = {ŷi}Ni=1: labels predicted by original NN classifier on training data σ = {σi = [p̂i,1, p̂i,2, . . . , p̂i,K ]}Ni=1: softmax outputs of original NN classifier on training data ĉ = {ĉi = max(σi)}Ni=1: maximum class probability returned by original NN classifier on training data x∗: data to be predicted σ∗: softmax outputs of original NN classifier on x∗ ĉ∗: maximum class probability returned by original NN classifier on x∗ Ensure: ĉ′∗ ∼ N (ĉ∗ + ¯̂r∗, var(r̂∗)): ĉ∗ + ¯̂r∗ can be used as confidence score for error detection, and var(r̂∗) represents the uncertainty of returned confidence score Training Phase: 1: obtain target confidence score c = {ci = δyi,ŷi}Ni=1, where δyi,ŷi is the Kronecker delta
(δyi,ŷi = 1 if yi = ŷi, otherwise δyi,ŷi = 0) 2: calculate residuals r = {ri = ci − ĉi}Ni=1 3: for each optimizer step do 4: calculate covariance matrix Kc((X ,σ), (X ,σ)), where each entry is given by
kc((xi, σi), (xj , σj)) = kin(xi,xj) + kout(σi, σj), for i, j = 1, 2, . . . , N 5: optimize GP hyperparameters by maximizing log marginal likelihood log p(r|X ,σ) =
− 12r >(Kc((X ,σ), (X ,σ)) + σ2nI)−1r− 12 log |Kc((X ,σ), (X ,σ)) + σ 2 nI| − n2 log 2π
Deployment Phase: 6: calculate residual mean ¯̂r∗ = k>∗ (Kc((X ,σ), (X ,σ)) + σ2nI)−1r and residual variance
var(r̂∗) = kc((x∗, σ∗), (x∗, σ∗)) − k>∗ (Kc((X ,σ), (X ,σ)) + σ2nI)−1k∗, where k∗ denotes the vector of kernel-based covariances (i.e., kc(x∗,xi)) between x∗ and all the training points
7: return distribution of calibrated confidence score ĉ′∗ ∼ N (ĉ∗ + ¯̂r∗, var(r̂∗))
such as those of the final softmax layer of a NN classifer, representing estimated class probabilities for each class. This modification allows RIO to access more information from the classifier outputs.
The targets for RIO training need to be redesigned as well. The raw targets of a classification problem are the ground-truth labels; they are in categorical space, while RIO works in continuous space. To solve this issue, RED constructs a different problem: Instead of predicting the labels directly, it predicts whether the original prediction is correct or not. A target confidence score is
assigned to each training data point accordingly. The residual between this target confidence score and the originally returned maximum class probability is calculated, and a RIO model is trained to predict these residuals. Given a new data point, the trained RIO model combined with the original NN classifier thus provides a calibrated confidence score for detecting misclassification errors.
Figure 1 illustrates the RED training and deployment processes conceptually, and Algorithm 1 specifies them in detail. In the training phase, the first step is to define a target confidence score ci for each training sample (xi, yi). For simplicity, all training samples that are correctly predicted by the original NN classifier receive 1 as the target confidence score, and those that are incorrectly predicted receive 0. The validation dataset during the original NN training is included in the training dataset for RED. After the target confidence scores are assigned, a regression problem is formulated for the RIO model: Given the original raw features {xi}Ni=1 and the corresponding softmax outputs of the original NN classifier {σi = [p̂i,1, p̂i,2, . . . , p̂i,K ]}Ni=1, predict the residuals r = {ri = ci − ĉi}ni=1 between target confidence scores c = {ci}Ni=1 and the original maximum class probabilities ĉ = {ĉi = max(σi)}Ni=1. The RIO model relies on an I/O kernel consisting of two components: the input kernel kin(xi,xj), which measures covariances in the raw feature space, and the modified multi-output kernel kout(σi, σj), which calculates covariances in the softmax output space. The hyperparameters of the I/O kernel are optimized to maximize the log marginal likelihood log p(r|X ,σ). In the deployment phase, given a new data point x∗, the trained RIO model provides a Gaussian distribution for the estimated residual r̂∗ ∼ N (¯̂r∗, var(r̂∗)). By adding the estimated residual back to the original maximum class probability ĉ∗, a distribution of calibrated confidence score is obtained as ĉ′∗ ∼ N (ĉ∗ + ¯̂r∗, var(r̂∗)). The mean ĉ∗ + ¯̂r∗ can be directly used as a quantitative metric for error detection, and the variance var(r̂∗) represents the corresponding uncertainty of the confidence score.

heading: 4 EMPIRICAL EVALUATION
text: In this section, the error detection performance of RED is evaluated comprehensively on 125 UCI datasets, comparing it to other related methods. Its generality is then evaluated by applying it to two other base models, and its scale-up properties measured in a larger deep learning architecture and in a larger task. Finally, RED’s potential to improve robustness more broadly is demonstrated in a case study focusing on OOD and adversarial samples.

heading: 4.1 COMPARISONS WITH RELATED APPROACHES
text: As a comprehensive evaluation of RED, an empirical comparison with seven related approaches on 125 UCI datasets (Dua & Graff, 2017) was performed. These approaches include maximum class probability baseline (MCP; Hendrycks & Gimpel, 2017), three state-of-the-art approaches, namely Trust Score (Jiang et al., 2018), ConfidNet (Corbière et al., 2019), and Introspection-Net (Aigrain & Detyniecki, 2019), as well as three earlier approaches, i.e. entropy of the original softmax outputs (Steinhardt & Liang, 2016), DNGO (Snoek et al., 2015), and the original SVGP (Hensman et al., 2013; 2015). The 125 UCI datasets include 121 datasets used by Klambauer et al. (2017) and four more recent ones. Full details about the datasets and parametric setup of all tested algorithms, and a downloadable link for source codes are provided in Appendix A.1.
Following the experimental setup of Hendrycks & Gimpel (2017); Corbière et al. (2019); Aigrain & Detyniecki (2019), the task for each algorithm is to provide a confidence score for each testing point. An error detector can then use a predefined fixed threshold on this score to decide which points are probably misclassified by the original NN classifier. For RED, the mean of calibrated confidence score ĉ∗ + ¯̂r∗ was used as the reported confidence score.
Five threshold-independent performance metrics were used to compare the methods: AUPR-Error, which computes the area under the Precision-Recall (AUPR) Curve when treating incorrect predictions as positive class during the detection; AUPR-Success, which is similar to AUPR-Error but uses correct predictions as positive class; AUROC, which computes the area under receiver operating characteristic (ROC) curve for the error detection task; AP-Error, which computes the average precision (AP) under different thresholds treating incorrect predictions as positive class; and AP-Success, which is similar to AP-Error but uses correct predictions as positive class. AUPR and AUROC are commonly used in prior work (Hendrycks & Gimpel, 2017; Corbière et al., 2019; Aigrain & De-
tyniecki, 2019), but as discussed by Davis & Goadrich (2006) and Flach & Kull (2015), AUPR may provide overly-optimistic measurement of performance. To compensate for this issue, AP-Error and AP-Success are included as additional metrics. Since the target for the confidence metrics is to detect misclassification errors, the following discussion will focus more on AP-Error and AUPR-Error.
Ten independent runs were conducted for each dataset. During each run, the dataset was randomly split into training dataset and testing dataset, and a standard NN classifier trained and evaluated on them. The same dataset split and trained NN classifier was used to evaluate all methods. Full details of the experimental setup are provided in Appendix A.1.
Table 1 shows the ranks of each algorithm averaged over all 125 UCI datasets. The rank of each algorithm on each dataset is based on the average performance over 10 independent runs. RED performs best on all metrics; the performance differences between RED and all other methods are statistically significant under paired t-test and Wilcoxon test. Trust Score has the highest standard deviation, suggesting that its performance varies significantly across different datasets.
As a more detailed comparison, Table 2 shows how often RED performs statistically significantly better, how often the performance is not significantly different, and how often it performs significantly worse than the other methods. RED is most often significantly better, and very rarely worse. In a handful of datasets Trust Score is better, but most often it is not.
To illustrate the performance of RED further compared to the baseline and the three state-of-theart approaches, Figure 2 shows the distribution of the relative rank of RED, MCP baseline, Trust Score, ConfidNet and Introspection-Net as a function of the number of samples and the number of features in the dataset. These plots are based on the AP-Error metric; other metrics provide similar results. RED performs consistently well over different dataset sizes and feature dimensionalities. Trust Score performs best in several datasets, but occasionally also worst in both small and large datasets, making it a rather unreliable choice. ConfidNet generally exhibits worse performance on datasets with large dataset sizes and high feature dimensionalities, i.e. it does not scale well to larger problems.

heading: 4.2 GENERALITY WRT. BASE MODELS
text: To evaluate generality and robustness of RED, it was applied to two other base models: an NN classifier using MC-dropout technique (Gal & Ghahramani, 2016) and a Bayesian Neural Network (BNN) classifier (Wen et al., 2018). They were each trained as base classifiers, and RED was then applied to each of them (implementation details are provided in Appendix A.1). Experiments analogous to those in Table 2 were performed on 125 UCI datasets in both cases. Table 3 shows the pairwise comparisons between RED and the internal confidence scores returned by the base models. MCP and Entropy represent the maximum class probability and entropy of softmax outputs, respectively, after averaging over 100 test-time samplings. RED significantly improves MC-dropout and BNN classifier in most datasets, demonstrating that it is a general technique that can be applied to a variety of models.

heading: 4.3 SCALING UP TO LARGER ARCHITECTURES
text: To confirm that the RED approach scales up to large deep learning architectures, a VGG16 model (Simonyan & Zisserman, 2015) was trained on the CIFAR-10 dataset using a state-of-the-art training pipeline (see Appendix A.2 for details). In order to remove the influence of feature extraction in image preprocessing and to make the comparison fair, all approaches used the same logit outputs of the trained VGG16 model as their input features. 10 independent runs are performed. During each run, a VGG16 model is trained, and all the methods are evaluated based on this VGG16 model.
Table 4 shows the results on the two main error detection performance metrics (note that the table lists absolute values instead of rankings along each metric). Trust Score performs much better than in previous literatures (Corbière et al., 2019). This difference may be due to the fact that logit outputs are used as input features here, whereas Corbière et al. (2019) utilized a higher dimensional feature space for Trust Score. Based on the results, RED significantly outperforms all the counterparts in both metrics. This result demonstrates the advantages of RED in scaling up to larger architectures.
Table 4: A Comparison based on the VGG16 Network Architecture on the CIFAR-10 Task
Metric RED MCP Baseline Trust Score ConfidNet Introspection-Net Entropy DNGO SVGP mean±std mean±std mean±std mean±std mean±std mean±std mean±std mean±std AP-Error(%) 49.88±1.99* 47.09±2.19 48.76±2.28 45.80±2.85 42.11±1.98 47.91±2.17 33.91±2.94 40.71±2.33 AUPR-Error(%) 49.79±2.00* 46.99±2.21 48.68±2.29 45.70±2.86 42.01±1.98 47.81±2.19 34.43±2.92 40.60±2.34
The symbols * indicates that the differences between the marked entry and all other counterparts are statistically significant at the 5% significance level for both paired t-test and Wilcoxon test. The best entries that are significantly better than all the others under both statistical test are marked in boldface.
0.075 0.080 0.085 0.090 0.095 0.100 0.105 0.110 0.115 variance
0.2
0.4
0.6
0.8
1.0
1.2
m ea
n mean/variance of RED confidence score for annealing
correct incorrect OOD adversarial
Figure 3: Identifying OOD and Adversarial Samples Based on Mean and Variance of Confidence Scores. Each dot represents one sample in the testing set in the UCI Annealing task. The horizontal axis denotes the variance of RED-calibrated confidence score, and the vertical axis denotes the mean. If an in-distribution sample is correctly classified by original NN classifier, it is marked as ”correct”, otherwise it is marked ”incorrect”. Mean is a good separator of correct and incorrect classifications. High variance, on the other hand, indicates that RED is uncertain about its confidence score, which can be used to identify OOD and adversarial samples. In this manner, RED can serve as a foundation for improving robustness of classifiers more broadly in the future.

heading: 4.4 A CASE STUDY WITH OOD AND ADVERSARIAL SAMPLES
text: In all experiments so far, the mean of calibrated confidence score ĉ∗ + ¯̂r∗ was used as RED’s confidence score. Although good performance is observed in error detection by only using the mean, the variance of calibrated confidence score var(r̂∗) may be helpful if the scenario is more complex, e.g., the dataset includes some OOD data, or even adversarial data.
A preliminary investigation of RED in such scenaria was performed by manually adding OOD and adversarial data into the test set of the UCI ”annealing” dataset, on which RED detected errors well. The synthetic OOD and adversarial samples were created to be highly deceptive, aiming to evaluate the performance of RED under difficult circumstances. The OOD data were sampled from a Gaussian distribution with mean 0 and variance 1, and the number of added OOD data was the same as the number of samples in the original test set. Note that all data points from original dataset are normalized to have mean 0 and variance 1 for each feature dimension during preprocessing, so the OOD data and in-distribution data have similar scales. The adversarial data was created by adding negligible modifications to training samples that the original NN classifier predicted incorrectly with highest confidence. This process mimics an adversary that can arbitrarily alter the output of the NN classifier with minuscule changes to the input (Goodfellow et al., 2014).
Figure 3 shows the distribution of mean and variance of calibrated confidence scores for testing samples, including correctly and incorrectly labeled actual samples, as well as the synthetic OOD and adversarial samples. The mean is a good separator for correctly classified and incorrectly classified samples, which tend to cluster on the top and bottom half of the image, respectively. On the
other hand, variance is a promising indicator of OOD and adversarial samples. RED’s confidence scores of in-distribution samples have low variance because they covary with the training samples. The variance thus represents RED’s confidence in its confidence score. Samples with large variance indicate that RED is uncertain about its confidence score, which can be used as a basis for detecting OOD and adversarial samples.
Thus, although the main focus of this paper is to demonstrate RED on misclassification detection, the preliminary results in this subsection show that it provides a promising foundation for detecting other error types as well.

heading: 5 DISCUSSION AND FUTURE WORK
text: One interesting observation from the experiments is that RED almost never performs worse than the MCP baseline. This result suggests that there is almost no risk in applying RED on top of an existing NN classifier. Since RED is based on a GP model, the estimated residual ¯̂r∗ is close to zero if the predicted sample is far from the distribution of the original training samples, resulting in no change to the original MCP. In other words, RED does not make random changes to original MCP if it is very uncertain about the predicted sample, and this uncertainty is explicitly represented in the variance of the estimated confidence score. This property makes RED a particularly reliable technique for error detection.
Another interesting observation is that the variance is also helpful in detecting OOD and adversarial samples. This result follows from the design of the RIO uncertainty model. Since RIO in RED has an input kernel and an output kernel, lower estimated variance requires that the predicted sample is close to training samples in both the input feature space and the classifier output space. This requirement is difficult for OOD and adversarial attacks to achieve, providing a basis for detecting them.
In a real-world deployment, it is necessary to define a threshold for triggering error warning based on RED’s confidence scores. A practical way is to use a validation dataset to determine how the precision/recall tradeoff changes over different thresholds. The user can then select a threshold based on their preference.
The most compelling direction of future work is to extend this capability of RED further. Instead of using a single dimensional confidence score for error detection, it is possible to use mean and variance simultaneously, leading to a two dimensional detection space. Further separation between OOD and adversarial samples may be possible by adding one more dimension, such as the ratio between input kernel output and output kernel output. Also, instead of using a hard target confidence score (i.e. either 0 or 1), it may be possible to define a soft target confidence that may be more informative. Further, RED may be used to calibrate other existing confidence metrics, such as the Trust Score, which may lead to a further improvement in detection performance.

heading: 6 CONCLUSION
text: This paper introduced a new framework, RED, for error detection in neural network classifiers that can produce a more reliable confidence score than previous methods. RED is able to not only provide a calibrated confidence score, but also report the uncertainty of the estimated confidence score. Experimental results show that RED’s scores consistently outperform state-of-the-art methods in separating the misclassified samples from correctly classified samples. Preliminary experiments also demonstrate that the approach scales up to large deep learning architectures, and can form a basis for detecting OOD and adversarial samples as well. It is therefore a promising foundation for improving robustness of neural network classifiers.

heading: A APPENDIX
text: A.1 EXPERIMENTAL SETUP FOR SECTION 4.1 AND SECTION 4.2
General Information 10 indenpendent runs are performed for each dataset. During each run, the dataset is randomly split into a training set (80%) and a testing set (20%), then a fully connected feedforward NN classifier with 2 hidden layers, each with 64 hidden neurons, are trained on the training set. The activation function is ReLU for all the hidden layers. The maximum number of epochs for training is 1000. 20% of the training set is used as validation set, and the split is random at each independent run. An early stop is triggered if the loss on validation set has not be improved for 10 epochs. The optimizer is Adam with learning rate 0.001, β1 = 0.9, and β2 = 0.999. The loss function is cross entropy loss. During each independent run, the same random dataset split and trained base NN classifier is used for evaluating all algorithms. All source codes for reproducing the experimental results can be downloaded from (https://drive.google.com/drive/folders/1X5R6sEkjmucR7B4MvFrY6QnCvos6a7n?usp=sharing).
Dataset Description In total, 125 UCI datasets are used in the experiments, among which 121 are from Klambauer et al. (2017), and 4 are recent datasets released in Dua & Graff (2017). All features in all datasets are normalized to have mean 0 and standard deviation 1. Full details regarding the number of samples N, number of features M, and number of classes K for each dataset are shown in Table 5.
Parametric Setup for Algorithms
• RED: SVGP (Hensman et al., 2013; 2015) is used as an approximator to original GP. The number of inducing points are 50. RBF kernel is used for both input and multi-output kernel. Automatic Relevance Determination (ARD) feature is turned on. The signal variances and length scales of all the kernels plus the noise variance are the trainable hyperparameters. The optimizer is L-BFGS-B with default parameters as in Scipy.optimize documentation (https://docs.scipy.org/doc/scipy/reference/optimize.minimize-lbfgsb.html), and the
maximum number of iterations is set as 1000. The optimization process runs until the L-BFGS-B optimizer decides to stop. To overcome the sensitivity of GP optimization to initialization of the hyperparameters (Ulapane et al., 2020), 20 random initialization of the hyperparameters are tried for each independent run. For each random initialization, the signal variances are generated from a uniform distribution within interval [0, 1], and the lengthscales are generated from a uniform distribution within interval [0, 10]. For 10 initializations, the hyperparameters of input kernel are first optimized while the multi-output kernel is temporarily turned off, then after the optimizer stops, the multi-output kernel is turned on, and both the two kernels are optimized simultaneously. For the other 10 initializations, both kernels are optimized simultaneouly from the start. The average performance of the 3 best optimized model in terms of corresponding metrics are used as the final performance of RED on each independent run. During our preliminary investigation, several statistic metrics on training set is effective in picking the true best-performing model out of these 20 trials, e.g., the gap between average estimated confidence scores of correctly classified training samples and incorrectly classified training samples, the scale of optimized noise variance of SVGP model, the ratio between sum of signal variances and noise variance after optimization, etc. Since improving initialization and optimization of GP hyperparameters is out of the scope of this work, we simply use average performance of the best 3 models (top 15%) in comparison.
• MCP baseline: The maximum class probability of softmax outputs of the base NN classifier is used as the confidence score of MCP baseline. The setup of the base NN classifier is provided above.
• Trust Score: k=10, α = 0, without filtering. This is the same as the default setup in https://github.com/google/TrustScore.
• ConfidNet: During training, the input to ConfidNet is the raw feature, and the target is the class probability of the ground-truth class returned by base NN classifier. The architecture of ConfidNet is a fully connected feed-forward NN regressor with 2 hidden layers, each with 64 hidden neurons. The activation function is ReLU for all the hidden layers. The maximum number of epochs for training is 1000. An early stop is triggered if the loss on validation data has not be improved for 10 epochs. The optimizer is RMSprop with learning rate 0.001, and the loss function is mean squared error (MSE).
• Introspection-Net: During training, the input to Introspection-Net is the logit outputs of base NN classifier, and the target is 1 for correctly classified sample, and 0 for incorrectly classified sample. The architecture of ConfidNet is a fully connected feed-forward NN regressor with 2 hidden layers, each with 64 hidden neurons. The activation function is ReLU for all the hidden layers. The maximum number of epochs for training is 1000. An early stop is triggered if the loss on validation data has not be improved for 10 epochs. The optimizer is RMSprop with learning rate 0.001, and the loss function is mean squared error (MSE).
• Entropy: The entropy of softmax outputs of the base NN classifier is used as the confidence score of Entropy. THe setup of the base NN classifier is provided above.
• DNGO: A Bayesian linear regression layer similar to that of Snoek et al. (2015) is added after the logits layer of the original NN classifier to predict whether an original prediction is correct or not (1 for correct and 0 for incorrect). Default parametric setup, as in https://github.com/automl/pybnn/blob/master/pybnn/dngo.py, is used.
• SVGP: The original SVGP without output kernel is used to predict directly whether a prediction made by the base NN classifier is correct or not (1 for correct and 0 for incorrect). All other parameters are identical to those in RED.
• BNN MCP: The standard dense layers in the base NN classifier described in RED setup above is replaced with Flipout layers (Wen et al., 2018). All other parameters are identical with those in RED. The maximum class probability averaging over 100 test-time samplings is used as the confidence score for error detection.
• BNN Entropy: The same setup as with BNN MCP, except now the entropy of softmax outputs averaging over 100 test-time samplings is used as the confidence score for error detection.
• MC-Dropout MCP: A dropout layer with dropout rate of 0.5 is added after each dense layer of the base NN classifier described in the RED setup. All other parameters are identical with those in RED. The maximum class probability averaging over 100 test-time MonteCarlo samplings is used as the confidence score for error detection. • MC-Dropout Entropy: The same setup as with MC-Dropout MCP, except now the entropy
of softmax outputs is averaged over 100 test-time Monte-Carlo samplings and used as confidence score for error detection.
A.2 EXPERIMENTAL SETUP FOR SECTION 4.3
Setup of VGG16 Training The standard VGG16 architecture (Simonyan & Zisserman, 2015) is used. The training pipeline is based on the default setup described in https://github.com/geifmany/cifar-vgg/blob/master/cifar10vgg.py. For the CIFAR-10 dataset, 40,000 samples are used as the training set, 10,000 as the validation set, and 10,000 as the testing set.
Parametric Setup for Algorithms All algorithms use the logit outputs of the trained VGG16 model as input features. The maximum class probability of softmax outputs of the trained VGG16 model is used as the confidence score of MCP baseline. The parameters for RED, Trust Score, Entropy, DNGO and SVGP are identical to those in the UCI experiments. For ConfidNet and Introspection-Net, all parameters are the same as in the UCI experiments, except for that the number of hidden neurons for all hidden layers is increased to 128.
A.3 DETAILED RESULTS FOR SECTION 4.1 AND SECTION 4.2
This subsection shows all the detailed results for experiments performed in Section 4.1 and Section 4.2. The results are averaged over 10 independent runs in terms of AP-Error, AP-Success, AUPR-Error, AUPR-Success, and AUROC. Detailed results for Section 4.1 are shown in Table 5, Table 6, Table 7, Table 8, and Table9. Detailed results for Section 4.2 are shown in Table 10, Table 11, Table 12, Table 13, and Table14. Each table is corresponding to one performance metric. The column ”N”, ”M”, and ”K” denotes the number of samples, number of features, and number of classes for corresponding datasets. To save space, ”MCP”, ”Intro-Net”, ”MC-D” stands for ”MCP Baseline”, ”Introspection-Net”, and ”MC-Dropout”, respectively. For datasets that the original NN classifier achieves 100% accuracy, the entries are marked as ”NA”. For dataset splits that the number of samples in one particular class is too small for Trust Score to calculate neighborhood distance, the entries are marked as ”NA”.
Table 5: Comparison between RED and Counterparts Using AP-Error
Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
abalone 4177 8 3 0.546 0.539 0.480 0.518 0.528 0.529 0.496 0.482 acute-inflammation 120 6 2 NA NA NA NA NA NA NA NA acute-nephritis 120 6 2 NA NA NA NA NA NA NA NA adult 48842 14 2 0.419 0.411 0.328 0.391 0.412 0.411 0.338 0.358 annealing 898 31 5 0.558 0.392 0.453 0.350 0.399 0.368 0.314 0.324 arrhythmia 452 262 13 0.583 0.580 0.618 0.409 0.413 0.560 0.426 0.475 audiology-std 196 59 18 0.779 0.721 NA 0.428 0.518 0.731 0.590 0.556 balance-scale 625 4 3 0.888 0.816 0.540 0.046 0.155 0.730 0.319 0.128 balloons 16 4 2 0.840 0.806 0.704 0.694 0.806 0.806 0.615 0.583 bank 4521 16 2 0.397 0.393 0.390 0.372 0.339 0.393 0.108 0.383 bioconcentration 779 9 3 0.493 0.470 0.510 0.426 0.461 0.440 0.408 0.385 blood 748 4 2 0.390 0.389 0.272 0.382 0.372 0.389 0.319 0.362 breast-cancer 286 9 2 0.532 0.503 0.381 0.467 0.431 0.503 0.441 0.471 breast-cancer-wisc 699 9 2 0.423 0.393 0.405 0.313 0.245 0.393 0.175 0.152 breast-cancer-wisc-diag 569 30 2 0.626 0.599 0.500 0.089 0.374 0.601 0.089 0.119 breast-cancer-wisc-prog 198 33 2 0.408 0.403 0.296 0.343 0.373 0.403 0.288 0.266 breast-tissue 106 9 6 0.813 0.743 0.800 0.696 0.601 0.749 0.744 0.722 car 1728 6 4 0.571 0.462 0.256 0.140 0.053 0.457 0.117 0.098 cardiotocography-10clases 2126 21 10 0.513 0.492 0.400 0.310 0.302 0.472 0.303 0.290 cardiotocography-3clases 2126 21 3 0.439 0.386 0.393 0.273 0.282 0.382 0.125 0.284 chess-krvk 28056 6 18 0.550 0.523 0.715 0.486 0.449 0.515 0.386 0.405 chess-krvkp 3196 36 2 0.347 0.339 0.163 0.037 0.087 0.339 0.038 0.054 climate 540 20 2 0.456 0.428 0.276 0.093 0.394 0.428 0.095 0.132 congressional-voting 435 16 2 0.468 0.465 0.429 0.474 0.459 0.464 0.478 0.470 conn-bench-sonar-mines-rocks 208 60 2 0.545 0.531 0.697 0.296 0.343 0.531 0.391 0.304 conn-bench-vowel-deterding 990 11 11 0.689 0.523 0.875 0.148 0.170 0.493 0.271 0.200
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Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
connect-4 67557 42 2 0.406 0.390 0.326 0.259 0.395 0.390 0.321 0.155 contrac 1473 9 3 0.596 0.590 0.529 0.552 0.584 0.577 0.573 0.548 credit-approval 690 15 2 0.365 0.359 0.336 0.264 0.331 0.359 0.302 0.336 cylinder-bands 512 35 2 0.494 0.482 0.506 0.345 0.422 0.482 0.383 0.304 dermatology 366 34 6 0.690 0.648 0.605 0.072 0.253 0.686 0.327 0.130 echocardiogram 131 10 2 0.510 0.413 0.325 0.353 0.407 0.413 0.306 0.375 ecoli 336 7 8 0.433 0.421 0.472 0.283 0.284 0.374 0.278 0.286 energy-y1 768 8 3 0.472 0.380 0.289 0.195 0.187 0.364 0.144 0.244 energy-y2 768 8 3 0.498 0.434 0.498 0.322 0.278 0.421 0.302 0.328 fertility 100 9 2 0.267 0.238 0.239 0.193 0.217 0.238 0.169 0.160 flags 194 28 8 0.767 0.717 0.784 0.666 0.664 0.712 0.649 0.659 glass 214 9 6 0.583 0.525 0.657 0.507 0.412 0.489 0.498 0.414 haberman-survival 306 3 2 0.452 0.446 0.386 0.412 0.425 0.446 0.360 0.499 hayes-roth 160 3 3 0.688 0.576 0.728 0.603 0.572 0.576 0.657 0.536 heart-cleveland 303 13 5 0.759 0.748 0.707 0.689 0.705 0.732 0.627 0.730 heart-hungarian 294 12 2 0.436 0.418 0.442 0.261 0.397 0.418 0.356 0.351 heart-switzerland 123 12 5 0.735 0.692 0.726 0.697 0.662 0.700 0.705 0.649 heart-va 200 12 5 0.757 0.732 0.676 0.678 0.732 0.746 0.677 0.639 hepatitis 155 19 2 0.542 0.441 0.495 0.423 0.330 0.441 0.408 0.369 hill-valley 1212 100 2 0.657 0.546 0.478 0.508 0.594 0.546 0.554 0.546 horse-colic 368 25 2 0.444 0.431 0.426 0.263 0.394 0.431 0.304 0.247 ilpd-indian-liver 583 9 2 0.470 0.459 0.427 0.434 0.443 0.459 0.375 0.445 image-segmentation 2310 18 7 0.539 0.455 0.471 0.251 0.214 0.430 0.218 0.139 ionosphere 351 33 2 0.543 0.493 0.335 0.158 0.405 0.493 0.300 0.171 iris 150 4 3 0.686 0.633 0.747 0.154 0.202 0.605 0.360 0.227 led-display 1000 7 10 0.572 0.571 0.301 0.553 0.512 0.547 0.508 0.470 lenses 24 4 3 0.875 0.900 0.786 0.665 0.790 0.900 0.622 0.587 letter 20000 16 26 0.495 0.461 0.785 0.245 0.081 0.465 0.109 0.164 libras 360 90 15 0.602 0.512 0.657 0.307 0.301 0.479 0.498 0.279 low-res-spect 531 100 9 0.552 0.515 0.454 0.304 0.305 0.504 0.297 0.343 lung-cancer 32 56 3 0.887 0.819 0.695 0.690 0.841 0.859 0.641 0.582 lymphography 148 18 4 0.660 0.546 0.619 0.278 0.386 0.545 0.353 0.363 magic 19020 10 2 0.366 0.355 0.294 0.309 0.350 0.355 0.240 0.233 mammographic 961 5 2 0.471 0.468 0.288 0.414 0.454 0.468 0.386 0.402 messidor 1151 19 2 0.465 0.449 0.359 0.364 0.426 0.449 0.411 0.350 miniboone 130064 50 2 0.380 0.361 0.190 0.299 0.371 0.361 0.251 0.162 molec-biol-promoter 106 57 2 0.550 0.541 0.786 0.330 0.505 0.541 0.432 0.437 molec-biol-splice 3190 60 3 0.461 0.455 0.375 0.193 0.208 0.464 0.396 0.174 monks-1 556 6 2 0.618 0.444 0.167 0.242 0.343 0.444 0.110 0.222 monks-2 601 6 2 0.524 0.466 0.611 0.417 0.426 0.466 0.389 0.387 monks-3 554 6 2 0.648 0.561 0.369 0.319 0.453 0.561 0.395 0.175 mushroom 8124 21 2 NA NA NA NA NA NA NA NA musk-1 476 166 2 0.518 0.508 0.501 0.101 0.354 0.508 0.295 0.103 musk-2 6598 166 2 0.409 0.372 0.083 0.009 0.077 0.372 0.006 0.007 nursery 12960 8 5 0.586 0.448 0.074 0.035 0.127 0.447 0.135 0.173 oocytes merluccius nucleus 4d 1022 41 2 0.442 0.401 0.277 0.296 0.383 0.401 0.385 0.330 oocytes merluccius states 2f 1022 25 3 0.505 0.488 0.413 0.256 0.358 0.477 0.214 0.262 oocytes trisopterus nucleus 2f 912 25 2 0.481 0.451 0.342 0.275 0.460 0.451 0.354 0.300 oocytes trisopterus states 5b 912 32 3 0.445 0.423 0.346 0.155 0.291 0.441 0.316 0.114 optical 5620 62 10 0.452 0.429 0.582 0.048 0.036 0.422 0.079 0.068 ozone 2536 72 2 0.308 0.293 0.270 0.207 0.146 0.293 0.032 0.025 page-blocks 5473 10 5 0.450 0.386 0.399 0.330 0.275 0.371 0.254 0.186 parkinsons 195 22 2 0.562 0.516 0.541 0.155 0.239 0.516 0.276 0.316 pendigits 10992 16 10 0.491 0.362 0.684 0.178 0.132 0.363 0.104 0.059 phishing 1353 9 3 0.453 0.433 0.369 0.347 0.371 0.412 0.338 0.334 pima 768 8 2 0.501 0.492 0.422 0.412 0.486 0.492 0.426 0.336 pittsburg-bridges-MATERIAL 106 7 3 0.600 0.501 0.485 0.360 0.511 0.506 0.336 0.392 pittsburg-bridges-REL-L 103 7 3 0.593 0.536 0.654 0.522 0.549 0.529 0.501 0.498 pittsburg-bridges-SPAN 92 7 3 0.582 0.550 0.543 0.367 0.507 0.563 0.472 0.449 pittsburg-bridges-T-OR-D 102 7 2 0.472 0.397 0.456 0.391 0.506 0.397 0.375 0.352 pittsburg-bridges-TYPE 105 7 6 0.809 0.736 0.758 0.684 0.662 0.692 0.702 0.737 planning 182 12 2 0.366 0.364 0.387 0.348 0.331 0.364 0.312 0.259 plant-margin 1600 64 100 0.586 0.572 0.696 0.331 0.266 0.569 0.305 0.183 plant-shape 1600 64 100 0.693 0.666 0.759 0.524 0.477 0.643 0.517 0.477 plant-texture 1599 64 100 0.607 0.596 0.708 0.308 0.291 0.610 0.282 0.221 post-operative 90 8 3 0.428 0.416 0.289 0.377 0.413 0.374 0.441 0.418 primary-tumor 330 17 15 0.794 0.783 0.693 0.725 0.721 0.779 0.683 0.715 ringnorm 7400 20 2 0.348 0.344 0.275 0.042 0.116 0.344 0.112 0.027 seeds 210 7 3 0.583 0.514 0.526 0.344 0.333 0.475 0.397 0.345 semeion 1593 256 10 0.541 0.527 0.704 0.092 0.237 0.564 0.180 0.142 soybean 683 35 18 0.573 0.516 0.448 0.317 0.191 0.505 0.234 0.319 spambase 4601 57 2 0.316 0.292 0.299 0.179 0.238 0.292 0.203 0.139 spect 265 22 2 0.520 0.506 0.384 0.443 0.471 0.506 0.419 0.434 spectf 267 44 2 0.478 0.427 0.429 0.251 0.428 0.427 0.258 0.346 statlog-australian-credit 690 14 2 0.473 0.455 0.418 0.417 0.416 0.455 0.407 0.430 statlog-german-credit 1000 24 2 0.423 0.422 0.383 0.329 0.389 0.422 0.354 0.254 statlog-heart 270 13 2 0.471 0.438 0.388 0.322 0.462 0.438 0.350 0.285
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Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
statlog-image 2310 18 7 0.561 0.464 0.484 0.240 0.114 0.473 0.274 0.183 statlog-landsat 6435 36 6 0.430 0.398 0.554 0.322 0.312 0.409 0.288 0.255 statlog-shuttle 58000 9 7 0.551 0.318 0.547 0.233 0.157 0.310 0.139 0.247 statlog-vehicle 846 18 4 0.542 0.526 0.319 0.392 0.361 0.536 0.346 0.364 steel-plates 1941 27 7 0.529 0.517 0.533 0.452 0.437 0.514 0.437 0.404 synthetic-control 600 60 6 0.647 0.541 0.462 0.052 0.117 0.498 0.199 0.031 teaching 151 5 3 0.646 0.604 0.540 0.632 0.593 0.612 0.676 0.569 thyroid 7200 21 3 0.442 0.413 0.160 0.126 0.190 0.409 0.015 0.114 tic-tac-toe 958 9 2 0.633 0.538 1.000 0.160 0.513 0.538 0.385 0.480 titanic 2201 3 2 0.314 0.313 0.202 0.310 0.315 0.313 0.227 0.321 trains 10 29 2 0.778 0.750 0.917 0.833 0.750 0.750 0.625 0.750 twonorm 7400 20 2 0.371 0.357 0.404 0.160 0.290 0.357 0.176 0.037 vertebral-column-2clases 310 6 2 0.501 0.490 0.346 0.378 0.432 0.490 0.389 0.273 vertebral-column-3clases 310 6 3 0.542 0.518 0.308 0.489 0.396 0.529 0.439 0.307 wall-following 5456 24 4 0.418 0.378 0.304 0.208 0.248 0.370 0.261 0.139 waveform 5000 21 3 0.465 0.423 0.331 0.357 0.394 0.423 0.200 0.162 waveform-noise 5000 40 3 0.428 0.398 0.322 0.230 0.326 0.397 0.215 0.167 wine 178 13 3 0.894 0.742 0.661 0.101 0.458 0.825 0.613 0.284 wine-quality-red 1599 11 6 0.534 0.522 0.540 0.494 0.491 0.496 0.523 0.453 wine-quality-white 4898 11 7 0.541 0.529 0.626 0.534 0.523 0.516 0.524 0.480 yeast 1484 8 10 0.570 0.563 0.510 0.506 0.498 0.548 0.410 0.912 zoo 101 16 7 0.910 0.804 0.793 0.297 0.546 0.893 0.443 0.694
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Table 6: Comparison between RED and Counterparts Using AP-Success
Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
abalone 4177 8 3 0.853 0.850 0.821 0.845 0.844 0.849 0.814 0.840 acute-inflammation 120 6 2 NA NA NA NA NA NA NA NA acute-nephritis 120 6 2 NA NA NA NA NA NA NA NA adult 48842 14 2 0.968 0.967 0.944 0.964 0.961 0.967 0.927 0.961 annealing 898 31 5 0.975 0.899 0.943 0.909 0.916 0.902 0.898 0.913 arrhythmia 452 262 13 0.817 0.810 0.809 0.654 0.668 0.803 0.673 0.778 audiology-std 196 59 18 0.946 0.926 NA 0.625 0.740 0.927 0.756 0.782 balance-scale 625 4 3 0.999 0.999 0.997 0.972 0.983 0.999 0.985 0.976 balloons 16 4 2 0.843 0.806 0.806 0.769 0.806 0.806 0.625 0.667 bank 4521 16 2 0.977 0.980 0.976 0.968 0.937 0.980 0.892 0.979 bioconcentration 779 9 3 0.746 0.728 0.782 0.717 0.726 0.721 0.693 0.698 blood 748 4 2 0.892 0.892 0.873 0.902 0.883 0.892 0.869 0.900 breast-cancer 286 9 2 0.820 0.820 0.765 0.796 0.775 0.820 0.767 0.785 breast-cancer-wisc 699 9 2 0.998 0.997 0.998 0.994 0.985 0.997 0.987 0.986 breast-cancer-wisc-diag 569 30 2 0.996 0.996 0.997 0.970 0.981 0.998 0.973 0.981 breast-cancer-wisc-prog 198 33 2 0.856 0.860 0.797 0.824 0.835 0.860 0.787 0.795 breast-tissue 106 9 6 0.909 0.878 0.898 0.863 0.767 0.872 0.800 0.849 car 1728 6 4 0.999 0.999 0.999 0.992 0.988 0.999 0.984 0.990 cardiotocography-10clases 2126 21 10 0.959 0.959 0.927 0.890 0.872 0.958 0.885 0.901 cardiotocography-3clases 2126 21 3 0.990 0.991 0.988 0.972 0.964 0.991 0.935 0.979 chess-krvk 28056 6 18 0.900 0.890 0.934 0.867 0.838 0.889 0.782 0.801 chess-krvkp 3196 36 2 1.000 1.000 0.995 0.990 0.994 1.000 0.993 0.996 climate 540 20 2 0.991 0.989 0.976 0.938 0.976 0.987 0.945 0.950 congressional-voting 435 16 2 0.693 0.690 0.672 0.692 0.684 0.689 0.691 0.689 conn-bench-sonar-mines-rocks 208 60 2 0.962 0.963 0.980 0.815 0.901 0.963 0.874 0.897 conn-bench-vowel-deterding 990 11 11 1.000 1.000 1.000 0.980 0.984 1.000 0.989 0.989 connect-4 67557 42 2 0.969 0.969 0.938 0.942 0.958 0.969 0.927 0.868 contrac 1473 9 3 0.717 0.714 0.581 0.682 0.675 0.705 0.658 0.669 credit-approval 690 15 2 0.943 0.941 0.916 0.909 0.936 0.941 0.896 0.928 cylinder-bands 512 35 2 0.865 0.862 0.872 0.789 0.809 0.862 0.767 0.758 dermatology 366 34 6 0.999 0.999 0.997 0.975 0.981 0.999 0.977 0.951 echocardiogram 131 10 2 0.944 0.936 0.925 0.916 0.924 0.936 0.888 0.929 ecoli 336 7 8 0.959 0.956 0.969 0.911 0.879 0.954 0.886 0.919 energy-y1 768 8 3 0.992 0.994 0.992 0.975 0.974 0.994 0.959 0.983 energy-y2 768 8 3 0.986 0.986 0.985 0.962 0.946 0.986 0.963 0.978 fertility 100 9 2 0.953 0.928 0.948 0.920 0.929 0.928 0.903 0.939 flags 194 28 8 0.739 0.716 0.817 0.573 0.617 0.720 0.580 0.599 glass 214 9 6 0.854 0.811 0.915 0.779 0.738 0.810 0.741 0.783 haberman-survival 306 3 2 0.850 0.849 0.854 0.821 0.835 0.849 0.818 0.835 hayes-roth 160 3 3 0.931 0.881 0.959 0.887 0.864 0.887 0.881 0.837 heart-cleveland 303 13 5 0.856 0.859 0.836 0.800 0.822 0.861 0.696 0.838 heart-hungarian 294 12 2 0.925 0.908 0.927 0.847 0.915 0.908 0.844 0.869 heart-switzerland 123 12 5 0.500 0.480 0.478 0.451 0.452 0.490 0.471 0.446 heart-va 200 12 5 0.526 0.499 0.462 0.439 0.504 0.511 0.417 0.392 hepatitis 155 19 2 0.965 0.965 0.975 0.930 0.896 0.965 0.897 0.915 hill-valley 1212 100 2 0.763 0.704 0.559 0.653 0.741 0.704 0.698 0.693 horse-colic 368 25 2 0.927 0.918 0.920 0.833 0.913 0.918 0.836 0.871
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Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
ilpd-indian-liver 583 9 2 0.878 0.874 0.814 0.860 0.863 0.874 0.800 0.871 image-segmentation 2310 18 7 0.997 0.997 0.998 0.983 0.984 0.997 0.981 0.985 ionosphere 351 33 2 0.990 0.988 0.976 0.900 0.973 0.988 0.931 0.934 iris 150 4 3 0.992 0.991 0.997 0.938 0.917 0.991 0.950 0.955 led-display 1000 7 10 0.883 0.883 0.759 0.874 0.850 0.880 0.835 0.847 lenses 24 4 3 0.982 0.960 0.944 0.871 0.883 0.960 0.859 0.844 letter 20000 16 26 0.995 0.996 0.999 0.980 0.947 0.996 0.963 0.982 libras 360 90 15 0.956 0.943 0.956 0.808 0.807 0.941 0.863 0.850 low-res-spect 531 100 9 0.977 0.968 0.953 0.908 0.892 0.968 0.902 0.950 lung-cancer 32 56 3 0.771 0.734 0.650 0.547 0.743 0.739 0.496 0.459 lymphography 148 18 4 0.941 0.925 0.958 0.809 0.861 0.924 0.865 0.826 magic 19020 10 2 0.966 0.966 0.954 0.961 0.959 0.966 0.907 0.946 mammographic 961 5 2 0.922 0.919 0.882 0.910 0.911 0.919 0.882 0.926 messidor 1151 19 2 0.863 0.861 0.764 0.806 0.830 0.861 0.808 0.806 miniboone 130064 50 2 0.992 0.992 0.983 0.988 0.986 0.992 0.972 0.981 molec-biol-promoter 106 57 2 0.892 0.888 0.953 0.724 0.880 0.888 0.784 0.822 molec-biol-splice 3190 60 3 0.953 0.953 0.926 0.833 0.846 0.952 0.901 0.838 monks-1 556 6 2 0.998 0.997 0.984 0.988 0.989 0.997 0.981 0.995 monks-2 601 6 2 0.885 0.857 0.918 0.809 0.810 0.857 0.761 0.808 monks-3 554 6 2 0.999 0.999 0.993 0.981 0.993 0.999 0.981 0.991 mushroom 8124 21 2 NA NA NA NA NA NA NA NA musk-1 476 166 2 0.984 0.983 0.986 0.912 0.957 0.983 0.942 0.934 musk-2 6598 166 2 1.000 1.000 0.998 0.995 0.995 1.000 0.994 0.994 nursery 12960 8 5 1.000 1.000 1.000 0.999 0.999 1.000 0.999 1.000 oocytes merluccius nucleus 4d 1022 41 2 0.939 0.930 0.881 0.892 0.904 0.930 0.890 0.907 oocytes merluccius states 2f 1022 25 3 0.991 0.991 0.991 0.967 0.970 0.991 0.950 0.975 oocytes trisopterus nucleus 2f 912 25 2 0.940 0.931 0.908 0.872 0.923 0.931 0.873 0.876 oocytes trisopterus states 5b 912 32 3 0.991 0.990 0.985 0.952 0.967 0.990 0.955 0.945 optical 5620 62 10 0.999 0.999 0.999 0.981 0.981 0.999 0.982 0.992 ozone 2536 72 2 0.994 0.996 0.993 0.988 0.966 0.996 0.968 0.951 page-blocks 5473 10 5 0.998 0.997 0.993 0.993 0.989 0.997 0.989 0.994 parkinsons 195 22 2 0.994 0.992 0.995 0.939 0.944 0.992 0.962 0.964 pendigits 10992 16 10 0.999 0.999 1.000 0.992 0.992 0.999 0.994 0.993 phishing 1353 9 3 0.975 0.973 0.958 0.959 0.946 0.972 0.929 0.952 pima 768 8 2 0.898 0.894 0.875 0.848 0.873 0.894 0.857 0.870 pittsburg-bridges-MATERIAL 106 7 3 0.976 0.969 0.953 0.921 0.933 0.969 0.892 0.939 pittsburg-bridges-REL-L 103 7 3 0.790 0.780 0.845 0.766 0.744 0.783 0.735 0.773 pittsburg-bridges-SPAN 92 7 3 0.851 0.808 0.785 0.678 0.756 0.805 0.782 0.716 pittsburg-bridges-T-OR-D 102 7 2 0.957 0.943 0.924 0.926 0.946 0.943 0.932 0.920 pittsburg-bridges-TYPE 105 7 6 0.818 0.692 0.747 0.651 0.635 0.664 0.737 0.766 planning 182 12 2 0.729 0.722 0.737 0.730 0.728 0.722 0.734 0.692 plant-margin 1600 64 100 0.951 0.950 0.966 0.828 0.817 0.950 0.816 0.731 plant-shape 1600 64 100 0.861 0.849 0.880 0.718 0.671 0.838 0.697 0.673 plant-texture 1599 64 100 0.953 0.950 0.958 0.809 0.804 0.950 0.789 0.791 post-operative 90 8 3 0.683 0.673 0.739 0.684 0.685 0.670 0.727 0.687 primary-tumor 330 17 15 0.762 0.752 0.654 0.640 0.633 0.750 0.546 0.642 ringnorm 7400 20 2 0.999 0.999 0.989 0.982 0.977 0.999 0.981 0.976 seeds 210 7 3 0.988 0.986 0.992 0.964 0.961 0.985 0.935 0.947 semeion 1593 256 10 0.994 0.994 0.996 0.927 0.950 0.994 0.943 0.963 soybean 683 35 18 0.996 0.995 0.993 0.970 0.942 0.995 0.944 0.974 spambase 4601 57 2 0.989 0.988 0.984 0.965 0.966 0.988 0.962 0.974 spect 265 22 2 0.811 0.802 0.767 0.731 0.788 0.801 0.686 0.733 spectf 267 44 2 0.955 0.951 0.942 0.913 0.919 0.953 0.879 0.949 statlog-australian-credit 690 14 2 0.701 0.692 0.656 0.668 0.678 0.692 0.629 0.662 statlog-german-credit 1000 24 2 0.890 0.891 0.873 0.837 0.850 0.891 0.821 0.782 statlog-heart 270 13 2 0.940 0.935 0.906 0.882 0.917 0.935 0.853 0.863 statlog-image 2310 18 7 0.998 0.998 0.998 0.986 0.980 0.998 0.984 0.990 statlog-landsat 6435 36 6 0.986 0.987 0.991 0.964 0.959 0.987 0.953 0.971 statlog-shuttle 58000 9 7 1.000 1.000 1.000 0.998 0.998 0.999 0.999 0.999 statlog-vehicle 846 18 4 0.966 0.967 0.927 0.929 0.913 0.968 0.870 0.922 steel-plates 1941 27 7 0.919 0.917 0.928 0.882 0.850 0.916 0.827 0.870 synthetic-control 600 60 6 1.000 1.000 0.999 0.986 0.988 1.000 0.988 0.986 teaching 151 5 3 0.686 0.648 0.598 0.657 0.640 0.634 0.682 0.632 thyroid 7200 21 3 0.999 0.999 0.996 0.992 0.984 0.999 0.985 0.996 tic-tac-toe 958 9 2 1.000 0.999 1.000 0.999 0.998 0.999 0.994 0.999 titanic 2201 3 2 0.877 0.876 0.805 0.874 0.873 0.876 0.821 0.877 trains 10 29 2 0.778 0.750 0.917 0.833 0.750 0.750 0.625 0.750 twonorm 7400 20 2 0.998 0.999 0.998 0.991 0.991 0.999 0.981 0.973 vertebral-column-2clases 310 6 2 0.977 0.977 0.953 0.952 0.942 0.977 0.917 0.937 vertebral-column-3clases 310 6 3 0.978 0.977 0.943 0.959 0.933 0.978 0.912 0.953 wall-following 5456 24 4 0.980 0.980 0.972 0.928 0.949 0.980 0.945 0.933 waveform 5000 21 3 0.968 0.966 0.947 0.947 0.941 0.966 0.866 0.848 waveform-noise 5000 40 3 0.962 0.962 0.935 0.881 0.900 0.962 0.872 0.844 wine 178 13 3 0.999 0.999 0.998 0.948 0.990 0.999 0.969 0.978 wine-quality-red 1599 11 6 0.735 0.727 0.751 0.705 0.696 0.719 0.695 0.697 wine-quality-white 4898 11 7 0.673 0.663 0.745 0.650 0.655 0.656 0.652 0.611 yeast 1484 8 10 0.756 0.751 0.706 0.718 0.692 0.745 0.601 0.579
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Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
zoo 101 16 7 0.999 0.998 0.997 0.928 0.963 0.999 0.963 0.950
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Table 7: Comparison between RED and Counterparts Using AUPR-Error
Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
abalone 4177 8 3 0.543 0.537 0.478 0.516 0.526 0.527 0.498 0.480 acute-inflammation 120 6 2 NA NA NA NA NA NA NA NA acute-nephritis 120 6 2 NA NA NA NA NA NA NA NA adult 48842 14 2 0.419 0.410 0.328 0.390 0.412 0.410 0.372 0.357 annealing 898 31 5 0.547 0.375 0.440 0.331 0.385 0.350 0.347 0.352 arrhythmia 452 262 13 0.573 0.570 0.606 0.396 0.399 0.547 0.412 0.465 audiology-std 196 59 18 0.766 0.704 NA 0.401 0.494 0.712 0.564 0.532 balance-scale 625 4 3 0.877 0.794 0.501 0.030 0.124 0.685 0.396 0.085 balloons 16 4 2 0.785 0.736 0.796 0.569 0.736 0.736 0.479 0.458 bank 4521 16 2 0.391 0.387 0.385 0.367 0.333 0.387 0.554 0.377 bioconcentration 779 9 3 0.485 0.461 0.501 0.418 0.453 0.431 0.398 0.376 blood 748 4 2 0.377 0.375 0.312 0.367 0.357 0.377 0.345 0.348 breast-cancer 286 9 2 0.518 0.483 0.354 0.447 0.405 0.483 0.421 0.452 breast-cancer-wisc 699 9 2 0.374 0.341 0.347 0.253 0.184 0.341 0.367 0.121 breast-cancer-wisc-diag 569 30 2 0.594 0.561 0.438 0.070 0.348 0.562 0.170 0.100 breast-cancer-wisc-prog 198 33 2 0.374 0.369 0.254 0.310 0.344 0.369 0.259 0.236 breast-tissue 106 9 6 0.795 0.717 0.771 0.660 0.556 0.722 0.717 0.690 car 1728 6 4 0.537 0.419 0.236 0.111 0.041 0.413 0.317 0.081 cardiotocography-10clases 2126 21 10 0.507 0.486 0.394 0.301 0.295 0.465 0.297 0.284 cardiotocography-3clases 2126 21 3 0.429 0.372 0.377 0.259 0.268 0.369 0.493 0.272 chess-krvk 28056 6 18 0.550 0.523 0.715 0.485 0.448 0.514 0.386 0.404 chess-krvkp 3196 36 2 0.308 0.299 0.141 0.024 0.067 0.299 0.245 0.036 climate 540 20 2 0.421 0.395 0.239 0.081 0.362 0.395 0.296 0.110 congressional-voting 435 16 2 0.503 0.503 0.659 0.505 0.543 0.488 0.503 0.511 conn-bench-sonar-mines-rocks 208 60 2 0.507 0.487 0.666 0.260 0.305 0.487 0.352 0.264 conn-bench-vowel-deterding 990 11 11 0.636 0.455 0.865 0.123 0.144 0.431 0.250 0.152 connect-4 67557 42 2 0.405 0.389 0.326 0.258 0.394 0.389 0.371 0.155 contrac 1473 9 3 0.591 0.585 0.525 0.546 0.579 0.573 0.569 0.544 credit-approval 690 15 2 0.344 0.337 0.316 0.241 0.311 0.337 0.289 0.316 cylinder-bands 512 35 2 0.481 0.464 0.493 0.331 0.406 0.464 0.373 0.288 dermatology 366 34 6 0.603 0.572 0.573 0.046 0.194 0.607 0.317 0.121 echocardiogram 131 10 2 0.463 0.350 0.274 0.303 0.357 0.350 0.236 0.325 ecoli 336 7 8 0.409 0.396 0.443 0.244 0.259 0.344 0.251 0.251 energy-y1 768 8 3 0.442 0.333 0.258 0.163 0.165 0.312 0.165 0.211 energy-y2 768 8 3 0.481 0.409 0.479 0.298 0.260 0.396 0.290 0.307 fertility 100 9 2 0.218 0.200 0.180 0.153 0.165 0.200 0.121 0.098 flags 194 28 8 0.760 0.703 0.774 0.651 0.649 0.697 0.632 0.647 glass 214 9 6 0.564 0.501 0.637 0.480 0.385 0.462 0.471 0.384 haberman-survival 306 3 2 0.426 0.420 0.363 0.389 0.400 0.420 0.334 0.477 hayes-roth 160 3 3 0.672 0.560 0.796 0.569 0.537 0.553 0.649 0.510 heart-cleveland 303 13 5 0.752 0.739 0.694 0.675 0.694 0.719 0.612 0.720 heart-hungarian 294 12 2 0.406 0.388 0.416 0.234 0.360 0.388 0.326 0.317 heart-switzerland 123 12 5 0.720 0.672 0.711 0.677 0.639 0.680 0.685 0.618 heart-va 200 12 5 0.748 0.720 0.658 0.664 0.721 0.736 0.665 0.621 hepatitis 155 19 2 0.499 0.383 0.435 0.375 0.273 0.383 0.365 0.317 hill-valley 1212 100 2 0.654 0.540 0.472 0.502 0.591 0.541 0.545 0.540 horse-colic 368 25 2 0.422 0.407 0.399 0.245 0.369 0.407 0.289 0.225 ilpd-indian-liver 583 9 2 0.458 0.445 0.410 0.419 0.428 0.445 0.433 0.429 image-segmentation 2310 18 7 0.527 0.434 0.449 0.229 0.200 0.409 0.208 0.123 ionosphere 351 33 2 0.513 0.452 0.303 0.128 0.364 0.452 0.286 0.148 iris 150 4 3 0.623 0.553 0.684 0.105 0.173 0.511 0.337 0.189 led-display 1000 7 10 0.574 0.573 0.566 0.557 0.512 0.548 0.504 0.469 lenses 24 4 3 0.833 0.887 0.798 0.572 0.759 0.887 0.530 0.499 letter 20000 16 26 0.493 0.459 0.784 0.242 0.125 0.463 0.110 0.162 libras 360 90 15 0.586 0.485 0.639 0.287 0.282 0.449 0.482 0.258 low-res-spect 531 100 9 0.536 0.493 0.437 0.281 0.288 0.479 0.274 0.314 lung-cancer 32 56 3 0.870 0.784 0.609 0.633 0.812 0.836 0.572 0.479 lymphography 148 18 4 0.641 0.512 0.582 0.228 0.339 0.514 0.302 0.323 magic 19020 10 2 0.364 0.353 0.293 0.307 0.348 0.353 0.293 0.232 mammographic 961 5 2 0.458 0.456 0.277 0.399 0.441 0.456 0.402 0.384 messidor 1151 19 2 0.458 0.442 0.350 0.356 0.417 0.442 0.404 0.341 miniboone 130064 50 2 0.379 0.361 0.190 0.299 0.370 0.361 0.349 0.162 molec-biol-promoter 106 57 2 0.506 0.497 0.765 0.292 0.436 0.497 0.399 0.396 molec-biol-splice 3190 60 3 0.457 0.450 0.372 0.189 0.203 0.459 0.398 0.171 monks-1 556 6 2 0.576 0.359 0.126 0.199 0.305 0.359 0.153 0.149 monks-2 601 6 2 0.512 0.452 0.622 0.405 0.412 0.452 0.376 0.374 monks-3 554 6 2 0.603 0.509 0.290 0.269 0.403 0.509 0.450 0.123 mushroom 8124 21 2 NA NA NA NA NA NA NA NA
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Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
musk-1 476 166 2 0.482 0.470 0.476 0.081 0.319 0.470 0.273 0.089 musk-2 6598 166 2 0.371 0.339 0.064 0.007 0.061 0.339 0.503 0.006 nursery 12960 8 5 0.550 0.388 0.070 0.027 0.129 0.386 0.368 0.131 oocytes merluccius nucleus 4d 1022 41 2 0.431 0.389 0.264 0.284 0.368 0.389 0.378 0.318 oocytes merluccius states 2f 1022 25 3 0.486 0.466 0.387 0.234 0.338 0.453 0.304 0.244 oocytes trisopterus nucleus 2f 912 25 2 0.470 0.439 0.329 0.262 0.447 0.439 0.349 0.285 oocytes trisopterus states 5b 912 32 3 0.420 0.394 0.321 0.137 0.266 0.417 0.316 0.101 optical 5620 62 10 0.437 0.411 0.572 0.043 0.037 0.402 0.070 0.067 ozone 2536 72 2 0.288 0.269 0.253 0.189 0.127 0.269 0.516 0.023 page-blocks 5473 10 5 0.439 0.374 0.388 0.317 0.263 0.359 0.255 0.173 parkinsons 195 22 2 0.490 0.456 0.477 0.108 0.197 0.456 0.264 0.277 pendigits 10992 16 10 0.480 0.344 0.677 0.168 0.124 0.343 0.102 0.050 phishing 1353 9 3 0.439 0.416 0.357 0.332 0.355 0.399 0.330 0.322 pima 768 8 2 0.489 0.480 0.408 0.399 0.477 0.480 0.414 0.322 pittsburg-bridges-MATERIAL 106 7 3 0.553 0.444 0.405 0.308 0.459 0.449 0.272 0.336 pittsburg-bridges-REL-L 103 7 3 0.565 0.498 0.624 0.485 0.519 0.491 0.464 0.452 pittsburg-bridges-SPAN 92 7 3 0.536 0.504 0.494 0.319 0.461 0.521 0.416 0.412 pittsburg-bridges-T-OR-D 102 7 2 0.388 0.299 0.376 0.320 0.462 0.299 0.298 0.274 pittsburg-bridges-TYPE 105 7 6 0.797 0.713 0.741 0.658 0.634 0.664 0.668 0.716 planning 182 12 2 0.339 0.337 0.362 0.315 0.295 0.337 0.281 0.229 plant-margin 1600 64 100 0.580 0.565 0.692 0.323 0.258 0.562 0.297 0.179 plant-shape 1600 64 100 0.690 0.662 0.757 0.519 0.475 0.639 0.513 0.472 plant-texture 1599 64 100 0.602 0.590 0.704 0.300 0.323 0.605 0.275 0.216 post-operative 90 8 3 0.391 0.375 0.230 0.329 0.368 0.334 0.398 0.368 primary-tumor 330 17 15 0.789 0.777 0.683 0.717 0.712 0.772 0.673 0.704 ringnorm 7400 20 2 0.334 0.329 0.269 0.040 0.111 0.329 0.285 0.025 seeds 210 7 3 0.515 0.447 0.453 0.282 0.284 0.405 0.340 0.314 semeion 1593 256 10 0.530 0.514 0.698 0.083 0.224 0.554 0.166 0.132 soybean 683 35 18 0.546 0.476 0.418 0.282 0.164 0.469 0.209 0.289 spambase 4601 57 2 0.307 0.282 0.290 0.170 0.228 0.282 0.211 0.131 spect 265 22 2 0.503 0.487 0.381 0.425 0.451 0.487 0.397 0.419 spectf 267 44 2 0.447 0.386 0.395 0.216 0.392 0.387 0.286 0.304 statlog-australian-credit 690 14 2 0.465 0.446 0.408 0.408 0.406 0.446 0.395 0.421 statlog-german-credit 1000 24 2 0.413 0.410 0.373 0.320 0.378 0.410 0.352 0.244 statlog-heart 270 13 2 0.443 0.406 0.351 0.288 0.435 0.406 0.329 0.255 statlog-image 2310 18 7 0.541 0.437 0.465 0.220 0.100 0.452 0.260 0.166 statlog-landsat 6435 36 6 0.425 0.393 0.551 0.317 0.306 0.404 0.291 0.251 statlog-shuttle 58000 9 7 0.534 0.294 0.527 0.214 0.183 0.281 0.347 0.229 statlog-vehicle 846 18 4 0.531 0.515 0.304 0.376 0.347 0.524 0.335 0.350 steel-plates 1941 27 7 0.523 0.511 0.528 0.446 0.430 0.508 0.432 0.397 synthetic-control 600 60 6 0.590 0.453 0.396 0.031 0.076 0.402 0.209 0.020 teaching 151 5 3 0.626 0.578 0.503 0.614 0.568 0.586 0.653 0.544 thyroid 7200 21 3 0.428 0.397 0.148 0.116 0.178 0.390 0.508 0.101 tic-tac-toe 958 9 2 0.562 0.473 1.000 0.132 0.461 0.473 0.423 0.418 titanic 2201 3 2 0.386 0.385 0.599 0.372 0.386 0.385 0.437 0.370 trains 10 29 2 0.667 0.625 0.875 0.750 0.625 0.625 0.438 0.625 twonorm 7400 20 2 0.359 0.345 0.394 0.152 0.278 0.345 0.270 0.034 vertebral-column-2clases 310 6 2 0.468 0.456 0.302 0.345 0.402 0.456 0.349 0.243 vertebral-column-3clases 310 6 3 0.505 0.478 0.279 0.460 0.366 0.495 0.411 0.271 wall-following 5456 24 4 0.414 0.373 0.299 0.204 0.243 0.364 0.263 0.135 waveform 5000 21 3 0.462 0.419 0.327 0.353 0.390 0.419 0.537 0.159 waveform-noise 5000 40 3 0.424 0.394 0.318 0.227 0.322 0.394 0.511 0.165 wine 178 13 3 0.857 0.669 0.606 0.056 0.413 0.794 0.602 0.181 wine-quality-red 1599 11 6 0.530 0.518 0.537 0.489 0.486 0.491 0.519 0.448 wine-quality-white 4898 11 7 0.539 0.527 0.625 0.532 0.521 0.514 0.523 0.478 yeast 1484 8 10 0.565 0.557 0.506 0.502 0.493 0.542 0.405 0.911 zoo 101 16 7 0.888 0.744 0.752 0.183 0.473 0.842 0.385 0.657
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Table 8: Comparison between RED and Counterparts Using AUPR-Success
Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
abalone 4177 8 3 0.852 0.850 0.821 0.844 0.844 0.849 0.806 0.840 acute-inflammation 120 6 2 NA NA NA NA NA NA NA NA acute-nephritis 120 6 2 NA NA NA NA NA NA NA NA adult 48842 14 2 0.968 0.967 0.944 0.964 0.961 0.967 0.897 0.961 annealing 898 31 5 0.975 0.903 0.942 0.907 0.914 0.899 0.908 0.919 arrhythmia 452 262 13 0.814 0.806 0.806 0.645 0.659 0.799 0.665 0.775 audiology-std 196 59 18 0.945 0.925 NA 0.609 0.725 0.925 0.741 0.769 balance-scale 625 4 3 0.999 0.999 0.997 0.971 0.983 0.999 0.986 0.976 balloons 16 4 2 0.773 0.736 0.861 0.690 0.736 0.736 0.516 0.542 bank 4521 16 2 0.977 0.980 0.976 0.968 0.936 0.980 0.946 0.979 bioconcentration 779 9 3 0.742 0.723 0.779 0.712 0.722 0.716 0.687 0.693 blood 748 4 2 0.891 0.891 0.872 0.901 0.882 0.891 0.881 0.899
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Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
breast-cancer 286 9 2 0.815 0.814 0.757 0.788 0.767 0.814 0.758 0.777 breast-cancer-wisc 699 9 2 0.998 0.997 0.998 0.994 0.984 0.997 0.992 0.986 breast-cancer-wisc-diag 569 30 2 0.996 0.997 0.997 0.969 0.980 0.998 0.970 0.981 breast-cancer-wisc-prog 198 33 2 0.850 0.855 0.787 0.817 0.828 0.855 0.779 0.785 breast-tissue 106 9 6 0.902 0.869 0.893 0.855 0.751 0.863 0.782 0.838 car 1728 6 4 0.999 0.999 0.999 0.992 0.988 0.999 0.989 0.989 cardiotocography-10clases 2126 21 10 0.959 0.959 0.927 0.889 0.871 0.958 0.884 0.901 cardiotocography-3clases 2126 21 3 0.990 0.991 0.988 0.971 0.963 0.991 0.963 0.979 chess-krvk 28056 6 18 0.900 0.890 0.934 0.867 0.837 0.889 0.782 0.801 chess-krvkp 3196 36 2 1.000 1.000 0.995 0.990 0.994 1.000 0.972 0.996 climate 540 20 2 0.991 0.989 0.976 0.937 0.975 0.986 0.958 0.949 congressional-voting 435 16 2 0.701 0.701 0.706 0.704 0.694 0.701 0.713 0.698 conn-bench-sonar-mines-rocks 208 60 2 0.961 0.963 0.980 0.806 0.898 0.963 0.852 0.894 conn-bench-vowel-deterding 990 11 11 1.000 1.000 1.000 0.979 0.984 1.000 0.991 0.989 connect-4 67557 42 2 0.969 0.969 0.938 0.942 0.958 0.969 0.883 0.867 contrac 1473 9 3 0.716 0.713 0.578 0.680 0.672 0.704 0.654 0.667 credit-approval 690 15 2 0.942 0.941 0.915 0.908 0.936 0.941 0.888 0.927 cylinder-bands 512 35 2 0.864 0.860 0.870 0.784 0.804 0.860 0.769 0.752 dermatology 366 34 6 0.999 0.999 0.997 0.975 0.980 0.999 0.976 0.950 echocardiogram 131 10 2 0.941 0.934 0.922 0.913 0.920 0.934 0.881 0.925 ecoli 336 7 8 0.958 0.955 0.969 0.908 0.874 0.953 0.881 0.916 energy-y1 768 8 3 0.992 0.993 0.992 0.975 0.973 0.993 0.960 0.983 energy-y2 768 8 3 0.986 0.986 0.985 0.961 0.945 0.986 0.960 0.978 fertility 100 9 2 0.951 0.923 0.946 0.914 0.924 0.923 0.893 0.936 flags 194 28 8 0.731 0.707 0.812 0.551 0.599 0.710 0.560 0.585 glass 214 9 6 0.850 0.804 0.912 0.767 0.725 0.802 0.729 0.775 haberman-survival 306 3 2 0.844 0.843 0.851 0.815 0.828 0.843 0.813 0.829 hayes-roth 160 3 3 0.932 0.880 0.965 0.882 0.854 0.885 0.864 0.821 heart-cleveland 303 13 5 0.853 0.856 0.832 0.792 0.818 0.858 0.683 0.835 heart-hungarian 294 12 2 0.923 0.905 0.926 0.841 0.912 0.905 0.838 0.862 heart-switzerland 123 12 5 0.464 0.440 0.429 0.399 0.414 0.448 0.435 0.408 heart-va 200 12 5 0.500 0.470 0.438 0.413 0.480 0.483 0.390 0.364 hepatitis 155 19 2 0.964 0.964 0.974 0.925 0.888 0.964 0.891 0.910 hill-valley 1212 100 2 0.761 0.702 0.554 0.650 0.739 0.702 0.702 0.690 horse-colic 368 25 2 0.926 0.916 0.917 0.827 0.911 0.916 0.825 0.869 ilpd-indian-liver 583 9 2 0.878 0.873 0.811 0.859 0.862 0.873 0.816 0.869 image-segmentation 2310 18 7 0.997 0.997 0.998 0.983 0.984 0.997 0.977 0.985 ionosphere 351 33 2 0.990 0.988 0.976 0.897 0.973 0.988 0.928 0.933 iris 150 4 3 0.992 0.991 0.997 0.935 0.912 0.990 0.945 0.954 led-display 1000 7 10 0.888 0.888 0.773 0.877 0.849 0.885 0.824 0.845 lenses 24 4 3 0.979 0.947 0.944 0.831 0.845 0.947 0.818 0.815 letter 20000 16 26 0.995 0.996 0.999 0.980 0.888 0.996 0.964 0.982 libras 360 90 15 0.955 0.942 0.955 0.802 0.800 0.940 0.859 0.848 low-res-spect 531 100 9 0.977 0.968 0.952 0.905 0.889 0.968 0.898 0.949 lung-cancer 32 56 3 0.728 0.687 0.572 0.461 0.703 0.688 0.393 0.356 lymphography 148 18 4 0.939 0.922 0.956 0.797 0.851 0.921 0.856 0.817 magic 19020 10 2 0.966 0.966 0.954 0.960 0.959 0.966 0.838 0.946 mammographic 961 5 2 0.922 0.918 0.881 0.909 0.910 0.918 0.865 0.925 messidor 1151 19 2 0.862 0.861 0.762 0.805 0.829 0.861 0.795 0.805 miniboone 130064 50 2 0.992 0.992 0.983 0.988 0.986 0.992 0.933 0.981 molec-biol-promoter 106 57 2 0.886 0.882 0.951 0.702 0.873 0.882 0.766 0.811 molec-biol-splice 3190 60 3 0.953 0.953 0.926 0.832 0.845 0.952 0.892 0.837 monks-1 556 6 2 0.998 0.997 0.982 0.988 0.989 0.997 0.983 0.995 monks-2 601 6 2 0.884 0.855 0.925 0.806 0.807 0.855 0.751 0.806 monks-3 554 6 2 0.999 0.999 0.992 0.980 0.993 0.999 0.977 0.991 mushroom 8124 21 2 NA NA NA NA NA NA NA NA musk-1 476 166 2 0.984 0.983 0.986 0.910 0.956 0.983 0.909 0.934 musk-2 6598 166 2 1.000 1.000 0.998 0.995 0.995 1.000 0.997 0.994 nursery 12960 8 5 1.000 1.000 1.000 0.999 0.999 1.000 0.999 1.000 oocytes merluccius nucleus 4d 1022 41 2 0.939 0.929 0.881 0.891 0.903 0.929 0.881 0.907 oocytes merluccius states 2f 1022 25 3 0.991 0.991 0.991 0.967 0.970 0.991 0.952 0.975 oocytes trisopterus nucleus 2f 912 25 2 0.939 0.931 0.907 0.870 0.922 0.931 0.858 0.874 oocytes trisopterus states 5b 912 32 3 0.991 0.990 0.985 0.951 0.967 0.990 0.932 0.945 optical 5620 62 10 0.999 0.999 0.999 0.981 0.978 0.999 0.980 0.992 ozone 2536 72 2 0.994 0.996 0.993 0.988 0.966 0.996 0.984 0.951 page-blocks 5473 10 5 0.998 0.997 0.993 0.993 0.989 0.997 0.966 0.994 parkinsons 195 22 2 0.994 0.992 0.995 0.936 0.942 0.992 0.960 0.963 pendigits 10992 16 10 0.999 0.999 1.000 0.992 0.992 0.999 0.994 0.993 phishing 1353 9 3 0.975 0.973 0.958 0.959 0.945 0.972 0.919 0.952 pima 768 8 2 0.897 0.893 0.873 0.845 0.871 0.893 0.839 0.869 pittsburg-bridges-MATERIAL 106 7 3 0.975 0.968 0.950 0.916 0.928 0.968 0.881 0.936 pittsburg-bridges-REL-L 103 7 3 0.776 0.766 0.832 0.741 0.725 0.769 0.715 0.755 pittsburg-bridges-SPAN 92 7 3 0.842 0.793 0.766 0.649 0.734 0.790 0.767 0.695 pittsburg-bridges-T-OR-D 102 7 2 0.956 0.941 0.919 0.924 0.945 0.941 0.931 0.916 pittsburg-bridges-TYPE 105 7 6 0.805 0.658 0.718 0.612 0.596 0.631 0.713 0.748 planning 182 12 2 0.715 0.709 0.723 0.714 0.713 0.709 0.720 0.677 plant-margin 1600 64 100 0.951 0.949 0.966 0.827 0.816 0.950 0.815 0.729
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Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
plant-shape 1600 64 100 0.860 0.849 0.879 0.715 0.668 0.837 0.693 0.669 plant-texture 1599 64 100 0.953 0.950 0.958 0.807 0.780 0.950 0.786 0.790 post-operative 90 8 3 0.653 0.642 0.719 0.656 0.660 0.639 0.702 0.654 primary-tumor 330 17 15 0.757 0.746 0.646 0.627 0.623 0.745 0.531 0.629 ringnorm 7400 20 2 0.999 0.999 0.989 0.982 0.977 0.999 0.959 0.976 seeds 210 7 3 0.987 0.986 0.991 0.962 0.960 0.985 0.929 0.945 semeion 1593 256 10 0.994 0.994 0.996 0.926 0.949 0.994 0.943 0.963 soybean 683 35 18 0.996 0.995 0.993 0.970 0.940 0.995 0.942 0.973 spambase 4601 57 2 0.989 0.988 0.983 0.965 0.966 0.988 0.941 0.974 spect 265 22 2 0.805 0.795 0.762 0.721 0.783 0.795 0.673 0.724 spectf 267 44 2 0.954 0.951 0.941 0.911 0.917 0.952 0.880 0.948 statlog-australian-credit 690 14 2 0.696 0.687 0.649 0.662 0.672 0.687 0.621 0.657 statlog-german-credit 1000 24 2 0.889 0.890 0.872 0.835 0.848 0.890 0.771 0.779 statlog-heart 270 13 2 0.939 0.934 0.904 0.878 0.914 0.934 0.829 0.860 statlog-image 2310 18 7 0.998 0.998 0.998 0.986 0.979 0.998 0.984 0.990 statlog-landsat 6435 36 6 0.986 0.987 0.991 0.963 0.959 0.987 0.948 0.971 statlog-shuttle 58000 9 7 1.000 1.000 1.000 0.998 0.907 0.999 0.999 0.999 statlog-vehicle 846 18 4 0.965 0.967 0.927 0.928 0.912 0.968 0.868 0.921 steel-plates 1941 27 7 0.919 0.916 0.928 0.881 0.848 0.915 0.824 0.870 synthetic-control 600 60 6 1.000 1.000 0.999 0.985 0.988 1.000 0.987 0.986 teaching 151 5 3 0.668 0.625 0.569 0.630 0.625 0.611 0.662 0.615 thyroid 7200 21 3 0.999 0.999 0.996 0.992 0.984 0.999 0.992 0.996 tic-tac-toe 958 9 2 1.000 0.999 1.000 0.999 0.998 0.999 0.994 0.999 titanic 2201 3 2 0.895 0.894 0.889 0.890 0.891 0.894 0.878 0.895 trains 10 29 2 0.667 0.625 0.875 0.750 0.625 0.625 0.438 0.625 twonorm 7400 20 2 0.998 0.999 0.998 0.991 0.991 0.999 0.924 0.973 vertebral-column-2clases 310 6 2 0.977 0.977 0.952 0.952 0.940 0.977 0.894 0.936 vertebral-column-3clases 310 6 3 0.977 0.977 0.943 0.958 0.931 0.977 0.908 0.952 wall-following 5456 24 4 0.980 0.980 0.972 0.928 0.949 0.980 0.941 0.932 waveform 5000 21 3 0.968 0.966 0.947 0.947 0.940 0.966 0.928 0.847 waveform-noise 5000 40 3 0.962 0.962 0.935 0.880 0.900 0.962 0.926 0.844 wine 178 13 3 0.999 0.999 0.998 0.945 0.990 0.999 0.968 0.978 wine-quality-red 1599 11 6 0.734 0.726 0.750 0.702 0.692 0.717 0.692 0.695 wine-quality-white 4898 11 7 0.672 0.662 0.745 0.648 0.654 0.655 0.650 0.610 yeast 1484 8 10 0.754 0.749 0.704 0.716 0.689 0.742 0.598 0.571 zoo 101 16 7 0.999 0.997 0.997 0.919 0.958 0.999 0.958 0.943
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Table 9: Comparison between RED and Counterparts Using AUROC
Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
abalone 4177 8 3 0.734 0.730 0.680 0.719 0.726 0.726 0.697 0.700 acute-inflammation 120 6 2 NA NA NA NA NA NA NA NA acute-nephritis 120 6 2 NA NA NA NA NA NA NA NA adult 48842 14 2 0.841 0.837 0.759 0.828 0.830 0.837 0.751 0.814 annealing 898 31 5 0.865 0.699 0.776 0.724 0.723 0.694 0.619 0.675 arrhythmia 452 262 13 0.712 0.715 0.727 0.505 0.517 0.703 0.528 0.639 audiology-std 196 59 18 0.883 0.848 NA 0.439 0.605 0.852 0.666 0.672 balance-scale 625 4 3 0.986 0.987 0.909 0.510 0.621 0.985 0.712 0.536 balloons 16 4 2 0.741 0.667 0.722 0.556 0.667 0.667 0.312 0.333 bank 4521 16 2 0.854 0.861 0.840 0.822 0.750 0.861 0.500 0.854 bioconcentration 779 9 3 0.621 0.607 0.660 0.572 0.595 0.588 0.547 0.541 blood 748 4 2 0.705 0.705 0.634 0.716 0.687 0.705 0.665 0.704 breast-cancer 286 9 2 0.682 0.677 0.587 0.637 0.609 0.677 0.607 0.620 breast-cancer-wisc 699 9 2 0.944 0.936 0.945 0.873 0.759 0.936 0.797 0.727 breast-cancer-wisc-diag 569 30 2 0.925 0.925 0.921 0.558 0.682 0.940 0.551 0.575 breast-cancer-wisc-prog 198 33 2 0.640 0.644 0.521 0.566 0.595 0.644 0.514 0.484 breast-tissue 106 9 6 0.864 0.815 0.850 0.788 0.651 0.811 0.761 0.785 car 1728 6 4 0.974 0.968 0.934 0.717 0.615 0.968 0.653 0.691 cardiotocography-10clases 2126 21 10 0.839 0.835 0.756 0.670 0.622 0.829 0.660 0.663 cardiotocography-3clases 2126 21 3 0.902 0.901 0.882 0.804 0.761 0.900 0.563 0.813 chess-krvk 28056 6 18 0.778 0.759 0.868 0.722 0.685 0.753 0.616 0.642 chess-krvkp 3196 36 2 0.967 0.963 0.777 0.573 0.642 0.963 0.568 0.688 climate 540 20 2 0.898 0.858 0.760 0.449 0.744 0.854 0.554 0.572 congressional-voting 435 16 2 0.603 0.600 0.562 0.607 0.595 0.598 0.619 0.612 conn-bench-sonar-mines-rocks 208 60 2 0.833 0.833 0.912 0.488 0.643 0.833 0.634 0.625 conn-bench-vowel-deterding 990 11 11 0.990 0.979 0.995 0.582 0.624 0.979 0.716 0.759 connect-4 67557 42 2 0.836 0.831 0.744 0.727 0.816 0.831 0.736 0.522 contrac 1473 9 3 0.663 0.658 0.556 0.619 0.642 0.645 0.626 0.608 credit-approval 690 15 2 0.741 0.741 0.683 0.649 0.703 0.741 0.625 0.694 cylinder-bands 512 35 2 0.703 0.702 0.713 0.579 0.637 0.702 0.561 0.516 dermatology 366 34 6 0.980 0.974 0.934 0.589 0.766 0.976 0.750 0.333 echocardiogram 131 10 2 0.758 0.709 0.655 0.634 0.687 0.709 0.597 0.659 ecoli 336 7 8 0.791 0.785 0.806 0.670 0.556 0.773 0.592 0.667
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Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
energy-y1 768 8 3 0.896 0.886 0.848 0.691 0.650 0.886 0.604 0.777 energy-y2 768 8 3 0.892 0.880 0.874 0.777 0.668 0.880 0.802 0.834 fertility 100 9 2 0.588 0.469 0.563 0.382 0.485 0.469 0.413 0.508 flags 194 28 8 0.727 0.696 0.791 0.603 0.618 0.698 0.598 0.574 glass 214 9 6 0.719 0.659 0.823 0.648 0.560 0.651 0.603 0.581 haberman-survival 306 3 2 0.694 0.692 0.651 0.645 0.671 0.692 0.636 0.684 hayes-roth 160 3 3 0.846 0.746 0.909 0.785 0.749 0.759 0.811 0.674 heart-cleveland 303 13 5 0.806 0.811 0.783 0.758 0.769 0.811 0.680 0.786 heart-hungarian 294 12 2 0.750 0.724 0.742 0.555 0.730 0.724 0.599 0.650 heart-switzerland 123 12 5 0.584 0.545 0.580 0.545 0.496 0.554 0.554 0.499 heart-va 200 12 5 0.629 0.606 0.544 0.522 0.603 0.618 0.505 0.455 hepatitis 155 19 2 0.822 0.797 0.845 0.703 0.619 0.797 0.624 0.677 hill-valley 1212 100 2 0.712 0.636 0.512 0.585 0.669 0.636 0.636 0.627 horse-colic 368 25 2 0.743 0.731 0.749 0.530 0.717 0.731 0.563 0.568 ilpd-indian-liver 583 9 2 0.719 0.709 0.650 0.686 0.695 0.709 0.614 0.709 image-segmentation 2310 18 7 0.935 0.936 0.946 0.770 0.742 0.935 0.734 0.749 ionosphere 351 33 2 0.909 0.886 0.779 0.504 0.786 0.886 0.630 0.535 iris 150 4 3 0.942 0.928 0.963 0.553 0.471 0.926 0.693 0.612 led-display 1000 7 10 0.762 0.762 0.545 0.749 0.717 0.753 0.706 0.700 lenses 24 4 3 0.917 0.875 0.863 0.677 0.740 0.875 0.625 0.583 letter 20000 16 26 0.932 0.940 0.977 0.795 0.526 0.940 0.646 0.775 libras 360 90 15 0.845 0.812 0.865 0.524 0.535 0.803 0.681 0.562 low-res-spect 531 100 9 0.864 0.837 0.791 0.618 0.559 0.836 0.616 0.717 lung-cancer 32 56 3 0.759 0.686 0.573 0.444 0.700 0.725 0.378 0.304 lymphography 148 18 4 0.805 0.753 0.825 0.503 0.622 0.748 0.623 0.566 magic 19020 10 2 0.810 0.807 0.753 0.788 0.796 0.807 0.639 0.720 mammographic 961 5 2 0.764 0.761 0.650 0.729 0.742 0.761 0.693 0.755 messidor 1151 19 2 0.697 0.690 0.565 0.609 0.662 0.690 0.646 0.601 miniboone 130064 50 2 0.905 0.903 0.806 0.878 0.882 0.903 0.781 0.783 molec-biol-promoter 106 57 2 0.735 0.727 0.885 0.414 0.719 0.727 0.505 0.576 molec-biol-splice 3190 60 3 0.810 0.810 0.722 0.517 0.547 0.811 0.717 0.501 monks-1 556 6 2 0.959 0.936 0.619 0.715 0.779 0.936 0.597 0.862 monks-2 601 6 2 0.751 0.698 0.814 0.616 0.632 0.698 0.602 0.598 monks-3 554 6 2 0.972 0.966 0.845 0.750 0.872 0.966 0.820 0.825 mushroom 8124 21 2 NA NA NA NA NA NA NA NA musk-1 476 166 2 0.863 0.860 0.863 0.449 0.724 0.860 0.635 0.503 musk-2 6598 166 2 0.984 0.981 0.794 0.513 0.627 0.982 0.500 0.417 nursery 12960 8 5 0.993 0.955 0.925 0.601 0.603 0.965 0.674 0.832 oocytes merluccius nucleus 4d 1022 41 2 0.779 0.758 0.627 0.652 0.717 0.758 0.708 0.693 oocytes merluccius states 2f 1022 25 3 0.907 0.909 0.907 0.763 0.795 0.908 0.706 0.793 oocytes trisopterus nucleus 2f 912 25 2 0.786 0.766 0.693 0.619 0.759 0.766 0.665 0.638 oocytes trisopterus states 5b 912 32 3 0.898 0.895 0.834 0.638 0.744 0.895 0.704 0.546 optical 5620 62 10 0.970 0.964 0.973 0.561 0.534 0.964 0.573 0.703 ozone 2536 72 2 0.899 0.905 0.851 0.773 0.568 0.906 0.500 0.315 page-blocks 5473 10 5 0.952 0.942 0.905 0.906 0.855 0.941 0.792 0.861 parkinsons 195 22 2 0.918 0.881 0.928 0.574 0.561 0.881 0.729 0.666 pendigits 10992 16 10 0.951 0.948 0.972 0.681 0.635 0.947 0.650 0.660 phishing 1353 9 3 0.859 0.852 0.788 0.798 0.762 0.847 0.726 0.766 pima 768 8 2 0.754 0.751 0.700 0.673 0.730 0.751 0.697 0.653 pittsburg-bridges-MATERIAL 106 7 3 0.848 0.771 0.776 0.586 0.706 0.772 0.535 0.692 pittsburg-bridges-REL-L 103 7 3 0.643 0.611 0.739 0.612 0.587 0.614 0.569 0.604 pittsburg-bridges-SPAN 92 7 3 0.689 0.634 0.631 0.403 0.574 0.631 0.579 0.488 pittsburg-bridges-T-OR-D 102 7 2 0.778 0.740 0.694 0.669 0.750 0.740 0.712 0.653 pittsburg-bridges-TYPE 105 7 6 0.799 0.713 0.743 0.657 0.636 0.673 0.723 0.728 planning 182 12 2 0.479 0.468 0.509 0.492 0.473 0.468 0.474 0.400 plant-margin 1600 64 100 0.846 0.844 0.888 0.605 0.559 0.844 0.583 0.391 plant-shape 1600 64 100 0.794 0.780 0.823 0.647 0.587 0.760 0.626 0.586 plant-texture 1599 64 100 0.854 0.848 0.883 0.580 0.560 0.850 0.534 0.491 post-operative 90 8 3 0.443 0.433 0.414 0.433 0.446 0.410 0.517 0.480 primary-tumor 330 17 15 0.769 0.765 0.676 0.689 0.673 0.764 0.621 0.689 ringnorm 7400 20 2 0.953 0.952 0.708 0.587 0.565 0.952 0.623 0.507 seeds 210 7 3 0.913 0.890 0.913 0.746 0.724 0.881 0.717 0.610 semeion 1593 256 10 0.930 0.926 0.953 0.499 0.662 0.928 0.615 0.657 soybean 683 35 18 0.945 0.942 0.916 0.765 0.598 0.937 0.614 0.807 spambase 4601 57 2 0.864 0.856 0.809 0.710 0.724 0.856 0.692 0.713 spect 265 22 2 0.668 0.660 0.581 0.562 0.620 0.660 0.524 0.558 spectf 267 44 2 0.798 0.788 0.740 0.650 0.727 0.789 0.621 0.760 statlog-australian-credit 690 14 2 0.585 0.569 0.543 0.537 0.539 0.569 0.510 0.525 statlog-german-credit 1000 24 2 0.715 0.720 0.679 0.620 0.676 0.720 0.636 0.513 statlog-heart 270 13 2 0.762 0.746 0.693 0.624 0.731 0.746 0.580 0.565 statlog-image 2310 18 7 0.943 0.947 0.941 0.818 0.683 0.947 0.777 0.784 statlog-landsat 6435 36 6 0.888 0.887 0.919 0.795 0.770 0.889 0.755 0.787 statlog-shuttle 58000 9 7 0.995 0.919 0.998 0.763 0.589 0.911 0.599 0.778 statlog-vehicle 846 18 4 0.854 0.856 0.719 0.759 0.716 0.858 0.682 0.730 steel-plates 1941 27 7 0.795 0.790 0.805 0.736 0.695 0.787 0.688 0.692 synthetic-control 600 60 6 0.984 0.980 0.956 0.497 0.547 0.977 0.671 0.497 teaching 151 5 3 0.645 0.596 0.555 0.618 0.574 0.585 0.662 0.553
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Dataset N M K RED MCP Trust Score ConfidNet Intro-Net Entropy DNGO SVGP
thyroid 7200 21 3 0.970 0.963 0.849 0.782 0.646 0.968 0.500 0.809 tic-tac-toe 958 9 2 0.986 0.933 1.000 0.876 0.894 0.933 0.684 0.942 titanic 2201 3 2 0.673 0.671 0.508 0.662 0.671 0.671 0.553 0.674 trains 10 29 2 0.556 0.500 0.833 0.667 0.500 0.500 0.250 0.500 twonorm 7400 20 2 0.952 0.952 0.941 0.817 0.812 0.952 0.662 0.499 vertebral-column-2clases 310 6 2 0.860 0.857 0.770 0.771 0.765 0.857 0.735 0.707 vertebral-column-3clases 310 6 3 0.877 0.873 0.715 0.819 0.710 0.877 0.726 0.750 wall-following 5456 24 4 0.850 0.847 0.793 0.641 0.706 0.846 0.700 0.603 waveform 5000 21 3 0.846 0.834 0.761 0.773 0.791 0.833 0.563 0.500 waveform-noise 5000 40 3 0.823 0.819 0.727 0.601 0.692 0.818 0.581 0.505 wine 178 13 3 0.991 0.975 0.954 0.345 0.799 0.980 0.751 0.646 wine-quality-red 1599 11 6 0.647 0.638 0.644 0.615 0.607 0.617 0.625 0.573 wine-quality-white 4898 11 7 0.621 0.612 0.677 0.604 0.605 0.597 0.600 0.553 yeast 1484 8 10 0.683 0.678 0.620 0.627 0.611 0.665 0.486 0.792 zoo 101 16 7 0.986 0.970 0.958 0.659 0.779 0.987 0.594 0.783
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Table 10: Comparison between RED and Counterparts Using AP-Error
Dataset N M K RED+BNN BNN MCP BNN Entropy RED+MC-D MC-D MCP MC-D Entropy
abalone 4177 8 3 0.529 0.522 0.513 0.516 0.509 0.503 acute-inflammation 120 6 2 NA NA NA NA NA NA acute-nephritis 120 6 2 NA NA NA NA NA NA adult 48842 14 2 0.415 0.407 0.406 0.416 0.405 0.405 annealing 898 31 5 0.788 0.424 0.338 0.590 0.421 0.375 arrhythmia 452 262 13 0.719 0.614 0.734 0.551 0.543 0.540 audiology-std 196 59 18 0.768 0.684 0.756 0.760 0.659 0.698 balance-scale 625 4 3 0.820 0.532 0.581 0.685 0.548 0.621 balloons 16 4 2 0.825 0.810 0.810 0.852 0.824 0.824 bank 4521 16 2 0.527 0.443 0.448 0.431 0.410 0.413 bioconcentration 779 9 3 0.562 0.487 0.486 0.462 0.440 0.418 blood 748 4 2 0.515 0.471 0.498 0.441 0.441 0.443 breast-cancer 286 9 2 0.504 0.396 0.421 0.518 0.486 0.497 breast-cancer-wisc 699 9 2 0.512 0.473 0.447 0.454 0.425 0.418 breast-cancer-wisc-diag 569 30 2 0.430 0.362 0.446 0.545 0.476 0.543 breast-cancer-wisc-prog 198 33 2 0.553 0.491 0.521 0.504 0.497 0.491 breast-tissue 106 9 6 0.847 0.777 0.797 0.885 0.841 0.832 car 1728 6 4 0.604 0.495 0.339 0.656 0.480 0.326 cardiotocography-10clases 2126 21 10 0.548 0.515 0.463 0.529 0.488 0.464 cardiotocography-3clases 2126 21 3 0.499 0.424 0.421 0.468 0.400 0.405 chess-krvk 28056 6 18 0.514 0.501 0.474 0.751 0.675 0.648 chess-krvkp 3196 36 2 0.392 0.369 0.385 0.443 0.417 0.424 climate 540 20 2 0.553 0.506 0.532 0.442 0.416 0.472 congressional-voting 435 16 2 0.517 0.466 0.472 0.486 0.485 0.496 conn-bench-sonar-mines-rocks 208 60 2 0.497 0.487 0.509 0.536 0.537 0.555 conn-bench-vowel-deterding 990 11 11 0.672 0.466 0.365 0.683 0.559 0.471 connect-4 67557 42 2 0.408 0.402 0.401 0.490 0.410 0.407 contrac 1473 9 3 0.634 0.629 0.622 0.605 0.599 0.594 credit-approval 690 15 2 0.388 0.359 0.365 0.414 0.402 0.409 cylinder-bands 512 35 2 0.517 0.436 0.422 0.459 0.462 0.469 dermatology 366 34 6 0.633 0.637 0.510 0.757 0.726 0.645 echocardiogram 131 10 2 0.440 0.407 0.443 0.533 0.488 0.507 ecoli 336 7 8 0.520 0.484 0.496 0.416 0.410 0.411 energy-y1 768 8 3 0.868 0.419 0.367 0.571 0.388 0.394 energy-y2 768 8 3 0.709 0.411 0.347 0.542 0.464 0.437 fertility 100 9 2 0.213 0.178 0.205 0.329 0.299 0.301 flags 194 28 8 0.794 0.750 0.746 0.764 0.718 0.728 glass 214 9 6 0.664 0.511 0.499 0.541 0.480 0.476 haberman-survival 306 3 2 0.476 0.337 0.349 0.492 0.489 0.482 hayes-roth 160 3 3 0.671 0.568 0.560 0.713 0.572 0.594 heart-cleveland 303 13 5 0.772 0.744 0.760 0.709 0.687 0.695 heart-hungarian 294 12 2 0.494 0.460 0.463 0.417 0.387 0.411 heart-switzerland 123 12 5 0.755 0.700 0.656 0.647 0.619 0.585 heart-va 200 12 5 0.833 0.812 0.828 0.753 0.715 0.733 hepatitis 155 19 2 0.610 0.559 0.585 0.616 0.582 0.564 hill-valley 1212 100 2 0.513 0.497 0.533 0.570 0.565 0.548 horse-colic 368 25 2 0.393 0.343 0.365 0.379 0.372 0.397 ilpd-indian-liver 583 9 2 0.499 0.395 0.428 0.481 0.470 0.472 image-segmentation 2310 18 7 0.543 0.372 0.371 0.533 0.456 0.406 ionosphere 351 33 2 0.367 0.316 0.448 0.436 0.360 0.394 iris 150 4 3 0.821 0.675 0.466 0.735 0.694 0.705 led-display 1000 7 10 0.577 0.566 0.521 0.574 0.571 0.523 lenses 24 4 3 0.805 0.662 0.674 0.979 0.875 0.854 letter 20000 16 26 0.461 0.455 0.401 0.705 0.627 0.563
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Dataset N M K RED+BNN BNN MCP BNN Entropy RED+MC-D MC-D MCP MC-D Entropy
libras 360 90 15 0.698 0.587 0.507 0.554 0.433 0.377 low-res-spect 531 100 9 0.623 0.545 0.604 0.575 0.541 0.563 lung-cancer 32 56 3 0.775 0.734 0.716 0.863 0.805 0.773 lymphography 148 18 4 0.456 0.427 0.412 0.507 0.492 0.467 magic 19020 10 2 0.358 0.354 0.356 0.358 0.350 0.353 mammographic 961 5 2 0.444 0.434 0.425 0.435 0.431 0.432 messidor 1151 19 2 0.472 0.455 0.443 0.414 0.401 0.399 miniboone 130064 50 2 0.373 0.360 0.360 0.374 0.364 0.366 molec-biol-promoter 106 57 2 0.642 0.520 0.569 0.560 0.491 0.500 molec-biol-splice 3190 60 3 0.441 0.428 0.399 0.471 0.434 0.445 monks-1 556 6 2 0.853 0.402 0.430 0.811 0.538 0.542 monks-2 601 6 2 0.896 0.389 0.429 0.527 0.416 0.427 monks-3 554 6 2 0.626 0.486 0.485 0.661 0.519 0.515 mushroom 8124 21 2 NA NA NA NA NA NA musk-1 476 166 2 0.651 0.481 0.549 0.454 0.421 0.382 musk-2 6598 166 2 0.540 0.389 0.447 0.456 0.351 0.406 nursery 12960 8 5 0.427 0.227 0.235 0.598 0.349 0.306 oocytes merluccius nucleus 4d 1022 41 2 0.813 0.487 0.470 0.407 0.387 0.387 oocytes merluccius states 2f 1022 25 3 0.464 0.404 0.358 0.449 0.426 0.468 oocytes trisopterus nucleus 2f 912 25 2 0.572 0.443 0.436 0.384 0.370 0.386 oocytes trisopterus states 5b 912 32 3 0.481 0.374 0.420 0.408 0.382 0.383 optical 5620 62 10 0.332 0.307 0.313 0.418 0.407 0.363 ozone 2536 72 2 0.321 0.240 0.285 0.350 0.351 0.337 page-blocks 5473 10 5 0.446 0.365 0.320 0.405 0.376 0.354 parkinsons 195 22 2 0.785 0.562 0.533 0.538 0.453 0.509 pendigits 10992 16 10 0.328 0.309 0.314 0.397 0.371 0.281 phishing 1353 9 3 0.604 0.451 0.454 0.559 0.471 0.444 pima 768 8 2 0.488 0.472 0.465 0.469 0.466 0.464 pittsburg-bridges-MATERIAL 106 7 3 0.485 0.410 0.460 0.548 0.507 0.500 pittsburg-bridges-REL-L 103 7 3 0.642 0.569 0.525 0.590 0.535 0.531 pittsburg-bridges-SPAN 92 7 3 0.593 0.579 0.622 0.545 0.498 0.520 pittsburg-bridges-T-OR-D 102 7 2 0.585 0.462 0.526 0.466 0.437 0.468 pittsburg-bridges-TYPE 105 7 6 0.834 0.758 0.755 0.784 0.725 0.720 planning 182 12 2 0.331 0.315 0.350 0.331 0.329 0.323 plant-margin 1600 64 100 0.623 0.603 0.509 0.614 0.603 0.476 plant-shape 1600 64 100 0.814 0.788 0.743 0.685 0.646 0.571 plant-texture 1599 64 100 0.659 0.650 0.556 0.654 0.624 0.503 post-operative 90 8 3 0.345 0.326 0.308 0.360 0.352 0.366 primary-tumor 330 17 15 0.880 0.854 0.845 0.809 0.803 0.801 ringnorm 7400 20 2 0.381 0.344 0.304 0.415 0.350 0.377 seeds 210 7 3 0.662 0.654 0.734 0.717 0.654 0.657 semeion 1593 256 10 0.552 0.538 0.479 0.538 0.514 0.499 soybean 683 35 18 0.555 0.522 0.393 0.534 0.519 0.410 spambase 4601 57 2 0.332 0.318 0.331 0.359 0.355 0.377 spect 265 22 2 0.473 0.427 0.418 0.518 0.513 0.518 spectf 267 44 2 0.784 0.581 0.575 0.430 0.362 0.355 statlog-australian-credit 690 14 2 0.471 0.360 0.347 0.446 0.414 0.421 statlog-german-credit 1000 24 2 0.602 0.547 0.557 0.418 0.412 0.422 statlog-heart 270 13 2 0.459 0.432 0.400 0.402 0.376 0.395 statlog-image 2310 18 7 0.548 0.422 0.351 0.508 0.460 0.403 statlog-landsat 6435 36 6 0.491 0.457 0.447 0.445 0.415 0.380 statlog-shuttle 58000 9 7 0.541 0.284 0.448 0.669 0.447 0.483 statlog-vehicle 846 18 4 0.546 0.532 0.439 0.526 0.493 0.450 steel-plates 1941 27 7 0.605 0.539 0.481 0.546 0.516 0.491 synthetic-control 600 60 6 0.439 0.310 0.713 0.584 0.426 0.463 teaching 151 5 3 0.727 0.694 0.693 0.699 0.650 0.626 thyroid 7200 21 3 0.561 0.452 0.488 0.547 0.489 0.478 tic-tac-toe 958 9 2 0.863 0.257 0.398 0.693 0.458 0.558 titanic 2201 3 2 0.367 0.346 0.338 0.344 0.344 0.346 trains 10 29 2 1.000 0.833 0.917 0.979 0.812 0.812 twonorm 7400 20 2 0.376 0.351 0.360 0.379 0.373 0.374 vertebral-column-2clases 310 6 2 0.549 0.502 0.509 0.477 0.463 0.476 vertebral-column-3clases 310 6 3 0.679 0.632 0.599 0.476 0.472 0.472 wall-following 5456 24 4 0.427 0.379 0.372 0.439 0.396 0.413 waveform 5000 21 3 0.418 0.395 0.368 0.432 0.424 0.392 waveform-noise 5000 40 3 0.441 0.430 0.398 0.440 0.429 0.406 wine 178 13 3 0.675 0.648 0.732 0.843 0.820 0.695 wine-quality-red 1599 11 6 0.578 0.532 0.521 0.521 0.502 0.492 wine-quality-white 4898 11 7 0.509 0.497 0.475 0.510 0.503 0.477 yeast 1484 8 10 0.605 0.597 0.576 0.596 0.590 0.549 zoo 101 16 7 0.907 0.821 0.808 0.931 0.802 0.847
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Table 11: Comparison between RED and Counterparts Using AP-Success
Dataset N M K RED+BNN BNN MCP BNN Entropy RED+MC-D MC-D MCP MC-D Entropy
abalone 4177 8 3 0.855 0.854 0.849 0.854 0.852 0.851 acute-inflammation 120 6 2 NA NA NA NA NA NA acute-nephritis 120 6 2 NA NA NA NA NA NA adult 48842 14 2 0.970 0.969 0.970 0.970 0.969 0.970 annealing 898 31 5 0.983 0.950 0.947 0.981 0.971 0.974 arrhythmia 452 262 13 0.805 0.756 0.811 0.831 0.814 0.827 audiology-std 196 59 18 0.920 0.881 0.939 0.943 0.906 0.927 balance-scale 625 4 3 0.999 0.996 0.997 0.999 0.998 0.999 balloons 16 4 2 0.869 0.845 0.845 0.880 0.861 0.861 bank 4521 16 2 0.985 0.980 0.981 0.981 0.982 0.983 bioconcentration 779 9 3 0.767 0.752 0.746 0.757 0.748 0.738 blood 748 4 2 0.903 0.885 0.905 0.902 0.903 0.905 breast-cancer 286 9 2 0.834 0.779 0.828 0.829 0.826 0.838 breast-cancer-wisc 699 9 2 0.998 0.992 0.996 0.998 0.998 0.997 breast-cancer-wisc-diag 569 30 2 0.997 0.990 0.997 0.998 0.994 0.998 breast-cancer-wisc-prog 198 33 2 0.879 0.856 0.840 0.863 0.862 0.864 breast-tissue 106 9 6 0.932 0.909 0.912 0.935 0.914 0.925 car 1728 6 4 0.997 0.998 0.998 0.999 1.000 0.999 cardiotocography-10clases 2126 21 10 0.960 0.954 0.952 0.962 0.956 0.955 cardiotocography-3clases 2126 21 3 0.990 0.988 0.989 0.992 0.991 0.992 chess-krvk 28056 6 18 0.923 0.919 0.912 0.806 0.753 0.738 chess-krvkp 3196 36 2 0.999 0.998 1.000 1.000 0.999 1.000 climate 540 20 2 0.989 0.986 0.993 0.987 0.987 0.990 congressional-voting 435 16 2 0.749 0.704 0.725 0.720 0.719 0.724 conn-bench-sonar-mines-rocks 208 60 2 0.940 0.931 0.953 0.955 0.951 0.957 conn-bench-vowel-deterding 990 11 11 0.998 0.994 0.990 0.998 0.996 0.996 connect-4 67557 42 2 0.975 0.975 0.975 0.959 0.954 0.954 contrac 1473 9 3 0.744 0.741 0.739 0.741 0.740 0.731 credit-approval 690 15 2 0.953 0.937 0.936 0.945 0.942 0.943 cylinder-bands 512 35 2 0.848 0.822 0.832 0.871 0.865 0.874 dermatology 366 34 6 0.998 0.992 0.999 1.000 1.000 0.999 echocardiogram 131 10 2 0.947 0.935 0.923 0.951 0.946 0.950 ecoli 336 7 8 0.958 0.950 0.964 0.957 0.956 0.959 energy-y1 768 8 3 0.997 0.978 0.979 0.996 0.993 0.993 energy-y2 768 8 3 0.993 0.973 0.973 0.990 0.986 0.987 fertility 100 9 2 0.959 0.923 0.928 0.962 0.935 0.939 flags 194 28 8 0.743 0.694 0.716 0.756 0.708 0.728 glass 214 9 6 0.837 0.732 0.747 0.837 0.793 0.829 haberman-survival 306 3 2 0.873 0.778 0.801 0.851 0.847 0.848 hayes-roth 160 3 3 0.974 0.969 0.963 0.955 0.904 0.905 heart-cleveland 303 13 5 0.893 0.881 0.886 0.867 0.865 0.869 heart-hungarian 294 12 2 0.934 0.913 0.929 0.926 0.908 0.911 heart-switzerland 123 12 5 0.588 0.539 0.517 0.502 0.476 0.469 heart-va 200 12 5 0.415 0.349 0.357 0.468 0.438 0.436 hepatitis 155 19 2 0.968 0.950 0.972 0.959 0.966 0.968 hill-valley 1212 100 2 0.523 0.509 0.525 0.551 0.548 0.543 horse-colic 368 25 2 0.927 0.893 0.911 0.906 0.896 0.903 ilpd-indian-liver 583 9 2 0.873 0.845 0.853 0.880 0.876 0.876 image-segmentation 2310 18 7 0.997 0.993 0.997 0.998 0.997 0.998 ionosphere 351 33 2 0.984 0.965 0.982 0.991 0.991 0.991 iris 150 4 3 0.997 0.994 0.993 0.997 0.996 0.997 led-display 1000 7 10 0.886 0.882 0.870 0.887 0.885 0.879 lenses 24 4 3 0.893 0.820 0.832 0.990 0.945 0.935 letter 20000 16 26 0.996 0.997 0.997 0.988 0.985 0.980 libras 360 90 15 0.879 0.847 0.732 0.952 0.939 0.925 low-res-spect 531 100 9 0.973 0.966 0.979 0.979 0.974 0.984 lung-cancer 32 56 3 0.715 0.685 0.695 0.727 0.648 0.592 lymphography 148 18 4 0.950 0.933 0.943 0.932 0.936 0.934 magic 19020 10 2 0.969 0.968 0.968 0.968 0.967 0.967 mammographic 961 5 2 0.925 0.909 0.916 0.922 0.919 0.920 messidor 1151 19 2 0.812 0.809 0.810 0.872 0.871 0.870 miniboone 130064 50 2 0.993 0.992 0.993 0.992 0.992 0.993 molec-biol-promoter 106 57 2 0.934 0.838 0.908 0.928 0.901 0.901 molec-biol-splice 3190 60 3 0.978 0.976 0.975 0.970 0.969 0.972 monks-1 556 6 2 1.000 0.996 0.997 1.000 0.999 0.999 monks-2 601 6 2 0.964 0.809 0.818 0.871 0.822 0.822 monks-3 554 6 2 0.997 0.996 0.996 0.999 0.997 0.997 mushroom 8124 21 2 NA NA NA NA NA NA musk-1 476 166 2 0.938 0.915 0.919 0.980 0.976 0.980 musk-2 6598 166 2 0.999 0.998 0.998 1.000 0.999 1.000 nursery 12960 8 5 1.000 1.000 1.000 1.000 1.000 1.000 oocytes merluccius nucleus 4d 1022 41 2 0.955 0.781 0.782 0.942 0.936 0.940 oocytes merluccius states 2f 1022 25 3 0.987 0.983 0.985 0.991 0.988 0.991 oocytes trisopterus nucleus 2f 912 25 2 0.914 0.850 0.866 0.936 0.926 0.934 oocytes trisopterus states 5b 912 32 3 0.985 0.977 0.980 0.993 0.991 0.993
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Dataset N M K RED+BNN BNN MCP BNN Entropy RED+MC-D MC-D MCP MC-D Entropy
optical 5620 62 10 0.997 0.997 0.999 0.999 0.998 0.999 ozone 2536 72 2 0.996 0.993 0.995 0.995 0.993 0.996 page-blocks 5473 10 5 0.998 0.997 0.998 0.998 0.998 0.998 parkinsons 195 22 2 0.988 0.975 0.977 0.986 0.987 0.987 pendigits 10992 16 10 0.999 0.999 0.999 0.999 0.999 0.999 phishing 1353 9 3 0.974 0.967 0.967 0.979 0.976 0.974 pima 768 8 2 0.897 0.890 0.906 0.901 0.899 0.904 pittsburg-bridges-MATERIAL 106 7 3 0.978 0.963 0.971 0.976 0.973 0.973 pittsburg-bridges-REL-L 103 7 3 0.809 0.772 0.731 0.798 0.786 0.791 pittsburg-bridges-SPAN 92 7 3 0.856 0.846 0.837 0.851 0.808 0.813 pittsburg-bridges-T-OR-D 102 7 2 0.956 0.910 0.937 0.965 0.944 0.945 pittsburg-bridges-TYPE 105 7 6 0.800 0.719 0.764 0.783 0.725 0.713 planning 182 12 2 0.775 0.761 0.774 0.713 0.707 0.699 plant-margin 1600 64 100 0.935 0.934 0.909 0.954 0.952 0.919 plant-shape 1600 64 100 0.714 0.685 0.622 0.874 0.857 0.804 plant-texture 1599 64 100 0.947 0.945 0.921 0.947 0.940 0.902 post-operative 90 8 3 0.685 0.664 0.666 0.692 0.688 0.688 primary-tumor 330 17 15 0.782 0.748 0.755 0.759 0.751 0.753 ringnorm 7400 20 2 0.999 0.999 0.999 0.998 0.997 0.998 seeds 210 7 3 0.980 0.976 0.991 0.990 0.986 0.985 semeion 1593 256 10 0.992 0.991 0.992 0.992 0.993 0.993 soybean 683 35 18 0.996 0.993 0.993 0.996 0.996 0.992 spambase 4601 57 2 0.987 0.986 0.990 0.989 0.988 0.992 spect 265 22 2 0.778 0.753 0.777 0.792 0.780 0.782 spectf 267 44 2 0.965 0.951 0.955 0.959 0.949 0.950 statlog-australian-credit 690 14 2 0.744 0.669 0.664 0.721 0.705 0.705 statlog-german-credit 1000 24 2 0.904 0.899 0.900 0.892 0.897 0.899 statlog-heart 270 13 2 0.937 0.926 0.930 0.933 0.926 0.931 statlog-image 2310 18 7 0.998 0.997 0.997 0.998 0.998 0.998 statlog-landsat 6435 36 6 0.984 0.982 0.981 0.985 0.985 0.985 statlog-shuttle 58000 9 7 1.000 0.999 1.000 1.000 1.000 1.000 statlog-vehicle 846 18 4 0.944 0.940 0.914 0.970 0.967 0.965 steel-plates 1941 27 7 0.923 0.911 0.900 0.923 0.913 0.916 synthetic-control 600 60 6 0.998 0.994 1.000 1.000 0.999 0.999 teaching 151 5 3 0.616 0.580 0.548 0.709 0.648 0.640 thyroid 7200 21 3 1.000 0.999 1.000 0.999 0.999 1.000 tic-tac-toe 958 9 2 1.000 0.998 0.999 1.000 0.999 0.999 titanic 2201 3 2 0.890 0.884 0.884 0.890 0.890 0.891 trains 10 29 2 1.000 0.833 0.917 0.979 0.812 0.812 twonorm 7400 20 2 0.999 0.999 0.999 0.999 0.998 0.999 vertebral-column-2clases 310 6 2 0.976 0.962 0.967 0.976 0.975 0.977 vertebral-column-3clases 310 6 3 0.971 0.965 0.965 0.976 0.976 0.976 wall-following 5456 24 4 0.990 0.987 0.990 0.992 0.992 0.994 waveform 5000 21 3 0.973 0.970 0.970 0.973 0.973 0.972 waveform-noise 5000 40 3 0.975 0.974 0.973 0.971 0.971 0.971 wine 178 13 3 0.997 0.990 0.999 0.999 0.998 0.999 wine-quality-red 1599 11 6 0.726 0.699 0.713 0.737 0.729 0.727 wine-quality-white 4898 11 7 0.640 0.626 0.609 0.665 0.654 0.635 yeast 1484 8 10 0.724 0.719 0.717 0.749 0.744 0.721 zoo 101 16 7 0.998 0.996 0.997 0.998 0.996 0.995
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Table 12: Comparison between RED and Counterparts Using AUPR-Error
Dataset N M K RED+BNN BNN MCP BNN Entropy RED+MC-D MC-D MCP MC-D Entropy
abalone 4177 8 3 0.526 0.519 0.510 0.513 0.506 0.501 acute-inflammation 120 6 2 NA NA NA NA NA NA acute-nephritis 120 6 2 NA NA NA NA NA NA adult 48842 14 2 0.414 0.406 0.405 0.415 0.405 0.404 annealing 898 31 5 0.785 0.414 0.327 0.580 0.401 0.353 arrhythmia 452 262 13 0.713 0.603 0.726 0.539 0.531 0.529 audiology-std 196 59 18 0.758 0.683 0.744 0.748 0.637 0.674 balance-scale 625 4 3 0.790 0.474 0.518 0.656 0.519 0.580 balloons 16 4 2 0.752 0.744 0.744 0.801 0.759 0.759 bank 4521 16 2 0.523 0.438 0.442 0.426 0.403 0.405 bioconcentration 779 9 3 0.557 0.477 0.477 0.454 0.427 0.408 blood 748 4 2 0.505 0.457 0.485 0.429 0.428 0.430 breast-cancer 286 9 2 0.488 0.372 0.394 0.498 0.459 0.476 breast-cancer-wisc 699 9 2 0.475 0.440 0.381 0.399 0.365 0.360 breast-cancer-wisc-diag 569 30 2 0.362 0.333 0.379 0.511 0.441 0.500 breast-cancer-wisc-prog 198 33 2 0.528 0.460 0.500 0.477 0.469 0.460 breast-tissue 106 9 6 0.830 0.751 0.772 0.877 0.825 0.813 car 1728 6 4 0.591 0.475 0.316 0.628 0.418 0.277 cardiotocography-10clases 2126 21 10 0.542 0.509 0.457 0.524 0.482 0.457
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Dataset N M K RED+BNN BNN MCP BNN Entropy RED+MC-D MC-D MCP MC-D Entropy
cardiotocography-3clases 2126 21 3 0.490 0.413 0.409 0.458 0.388 0.392 chess-krvk 28056 6 18 0.514 0.500 0.473 0.751 0.674 0.648 chess-krvkp 3196 36 2 0.338 0.320 0.312 0.395 0.373 0.378 climate 540 20 2 0.523 0.486 0.489 0.406 0.374 0.431 congressional-voting 435 16 2 0.503 0.450 0.457 0.471 0.470 0.481 conn-bench-sonar-mines-rocks 208 60 2 0.463 0.460 0.466 0.494 0.496 0.510 conn-bench-vowel-deterding 990 11 11 0.648 0.439 0.337 0.665 0.541 0.435 connect-4 67557 42 2 0.407 0.401 0.401 0.490 0.409 0.407 contrac 1473 9 3 0.631 0.626 0.619 0.601 0.595 0.590 credit-approval 690 15 2 0.367 0.334 0.344 0.393 0.380 0.386 cylinder-bands 512 35 2 0.506 0.423 0.406 0.443 0.447 0.454 dermatology 366 34 6 0.593 0.646 0.423 0.682 0.639 0.535 echocardiogram 131 10 2 0.394 0.360 0.378 0.477 0.435 0.452 ecoli 336 7 8 0.491 0.454 0.462 0.389 0.383 0.371 energy-y1 768 8 3 0.861 0.398 0.346 0.552 0.353 0.365 energy-y2 768 8 3 0.698 0.392 0.330 0.521 0.445 0.410 fertility 100 9 2 0.156 0.125 0.165 0.270 0.253 0.256 flags 194 28 8 0.785 0.737 0.732 0.754 0.705 0.715 glass 214 9 6 0.651 0.493 0.478 0.522 0.456 0.447 haberman-survival 306 3 2 0.455 0.315 0.321 0.468 0.465 0.457 hayes-roth 160 3 3 0.638 0.518 0.520 0.686 0.535 0.555 heart-cleveland 303 13 5 0.764 0.730 0.750 0.700 0.674 0.683 heart-hungarian 294 12 2 0.463 0.431 0.437 0.387 0.356 0.380 heart-switzerland 123 12 5 0.741 0.680 0.633 0.628 0.597 0.561 heart-va 200 12 5 0.827 0.803 0.822 0.744 0.703 0.720 hepatitis 155 19 2 0.575 0.523 0.536 0.583 0.539 0.508 hill-valley 1212 100 2 0.508 0.491 0.529 0.566 0.561 0.543 horse-colic 368 25 2 0.365 0.314 0.339 0.354 0.347 0.372 ilpd-indian-liver 583 9 2 0.487 0.382 0.415 0.465 0.454 0.457 image-segmentation 2310 18 7 0.527 0.352 0.352 0.517 0.432 0.382 ionosphere 351 33 2 0.314 0.283 0.387 0.380 0.287 0.314 iris 150 4 3 0.772 0.624 0.381 0.686 0.631 0.619 led-display 1000 7 10 0.571 0.559 0.513 0.568 0.565 0.513 lenses 24 4 3 0.757 0.553 0.559 0.974 0.833 0.807 letter 20000 16 26 0.458 0.452 0.397 0.705 0.626 0.562 libras 360 90 15 0.689 0.572 0.492 0.534 0.403 0.347 low-res-spect 531 100 9 0.610 0.523 0.588 0.557 0.517 0.543 lung-cancer 32 56 3 0.730 0.686 0.663 0.840 0.768 0.725 lymphography 148 18 4 0.411 0.381 0.363 0.460 0.442 0.423 magic 19020 10 2 0.356 0.353 0.354 0.357 0.348 0.351 mammographic 961 5 2 0.432 0.420 0.409 0.419 0.415 0.417 messidor 1151 19 2 0.466 0.447 0.436 0.405 0.391 0.389 miniboone 130064 50 2 0.372 0.360 0.360 0.374 0.364 0.366 molec-biol-promoter 106 57 2 0.608 0.518 0.526 0.506 0.435 0.450 molec-biol-splice 3190 60 3 0.435 0.420 0.392 0.465 0.426 0.439 monks-1 556 6 2 0.827 0.354 0.377 0.788 0.474 0.477 monks-2 601 6 2 0.895 0.379 0.419 0.515 0.401 0.413 monks-3 554 6 2 0.596 0.446 0.446 0.627 0.468 0.462 mushroom 8124 21 2 NA NA NA NA NA NA musk-1 476 166 2 0.640 0.460 0.534 0.428 0.389 0.340 musk-2 6598 166 2 0.533 0.378 0.438 0.435 0.328 0.381 nursery 12960 8 5 0.340 0.166 0.162 0.578 0.318 0.275 oocytes merluccius nucleus 4d 1022 41 2 0.810 0.478 0.461 0.394 0.373 0.374 oocytes merluccius states 2f 1022 25 3 0.445 0.387 0.339 0.426 0.402 0.446 oocytes trisopterus nucleus 2f 912 25 2 0.564 0.432 0.426 0.371 0.356 0.373 oocytes trisopterus states 5b 912 32 3 0.466 0.352 0.400 0.378 0.348 0.346 optical 5620 62 10 0.315 0.291 0.289 0.402 0.390 0.344 ozone 2536 72 2 0.303 0.221 0.265 0.330 0.336 0.311 page-blocks 5473 10 5 0.436 0.352 0.308 0.394 0.360 0.342 parkinsons 195 22 2 0.769 0.521 0.482 0.465 0.371 0.454 pendigits 10992 16 10 0.309 0.287 0.292 0.382 0.355 0.261 phishing 1353 9 3 0.596 0.437 0.440 0.551 0.457 0.434 pima 768 8 2 0.477 0.459 0.451 0.457 0.454 0.452 pittsburg-bridges-MATERIAL 106 7 3 0.415 0.318 0.379 0.496 0.450 0.435 pittsburg-bridges-REL-L 103 7 3 0.618 0.535 0.486 0.558 0.501 0.492 pittsburg-bridges-SPAN 92 7 3 0.550 0.534 0.582 0.499 0.449 0.463 pittsburg-bridges-T-OR-D 102 7 2 0.530 0.399 0.466 0.387 0.355 0.397 pittsburg-bridges-TYPE 105 7 6 0.825 0.737 0.738 0.770 0.704 0.694 planning 182 12 2 0.298 0.284 0.318 0.297 0.296 0.291 plant-margin 1600 64 100 0.619 0.597 0.502 0.610 0.598 0.469 plant-shape 1600 64 100 0.813 0.787 0.740 0.682 0.643 0.566 plant-texture 1599 64 100 0.655 0.646 0.550 0.650 0.620 0.496 post-operative 90 8 3 0.301 0.277 0.253 0.307 0.300 0.323 primary-tumor 330 17 15 0.878 0.850 0.841 0.804 0.797 0.794 ringnorm 7400 20 2 0.370 0.331 0.290 0.405 0.341 0.367 seeds 210 7 3 0.640 0.644 0.711 0.687 0.610 0.610 semeion 1593 256 10 0.543 0.527 0.465 0.523 0.496 0.483
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Dataset N M K RED+BNN BNN MCP BNN Entropy RED+MC-D MC-D MCP MC-D Entropy
soybean 683 35 18 0.524 0.487 0.354 0.498 0.482 0.368 spambase 4601 57 2 0.324 0.311 0.323 0.351 0.347 0.369 spect 265 22 2 0.452 0.404 0.394 0.498 0.492 0.497 spectf 267 44 2 0.775 0.560 0.545 0.396 0.322 0.314 statlog-australian-credit 690 14 2 0.460 0.350 0.335 0.435 0.403 0.409 statlog-german-credit 1000 24 2 0.595 0.540 0.547 0.407 0.400 0.412 statlog-heart 270 13 2 0.440 0.409 0.367 0.366 0.338 0.359 statlog-image 2310 18 7 0.532 0.404 0.330 0.491 0.439 0.373 statlog-landsat 6435 36 6 0.488 0.453 0.444 0.441 0.411 0.375 statlog-shuttle 58000 9 7 0.526 0.261 0.427 0.657 0.431 0.465 statlog-vehicle 846 18 4 0.536 0.522 0.426 0.513 0.478 0.437 steel-plates 1941 27 7 0.601 0.533 0.475 0.541 0.510 0.485 synthetic-control 600 60 6 0.391 0.233 0.684 0.510 0.340 0.386 teaching 151 5 3 0.710 0.672 0.673 0.680 0.632 0.605 thyroid 7200 21 3 0.552 0.439 0.473 0.536 0.476 0.461 tic-tac-toe 958 9 2 0.851 0.187 0.344 0.642 0.379 0.505 titanic 2201 3 2 0.361 0.340 0.332 0.338 0.338 0.340 trains 10 29 2 1.000 0.750 0.875 0.969 0.719 0.719 twonorm 7400 20 2 0.364 0.338 0.348 0.367 0.361 0.361 vertebral-column-2clases 310 6 2 0.523 0.465 0.475 0.440 0.422 0.434 vertebral-column-3clases 310 6 3 0.659 0.613 0.570 0.436 0.431 0.437 wall-following 5456 24 4 0.422 0.373 0.365 0.432 0.388 0.406 waveform 5000 21 3 0.414 0.391 0.363 0.428 0.419 0.387 waveform-noise 5000 40 3 0.437 0.425 0.393 0.436 0.425 0.401 wine 178 13 3 0.635 0.626 0.623 0.822 0.810 0.648 wine-quality-red 1599 11 6 0.574 0.527 0.516 0.517 0.497 0.487 wine-quality-white 4898 11 7 0.507 0.495 0.473 0.508 0.502 0.475 yeast 1484 8 10 0.602 0.594 0.573 0.592 0.586 0.544 zoo 101 16 7 0.888 0.771 0.764 0.904 0.733 0.819
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Table 13: Comparison between RED and Counterparts Using AUPR-Success
Dataset N M K RED+BNN BNN MCP BNN Entropy RED+MC-D MC-D MCP MC-D Entropy
abalone 4177 8 3 0.855 0.853 0.849 0.853 0.852 0.851 acute-inflammation 120 6 2 NA NA NA NA NA NA acute-nephritis 120 6 2 NA NA NA NA NA NA adult 48842 14 2 0.970 0.969 0.970 0.970 0.969 0.970 annealing 898 31 5 0.983 0.951 0.947 0.981 0.972 0.974 arrhythmia 452 262 13 0.801 0.750 0.806 0.827 0.809 0.823 audiology-std 196 59 18 0.918 0.912 0.938 0.942 0.902 0.925 balance-scale 625 4 3 0.999 0.996 0.997 0.999 0.998 0.999 balloons 16 4 2 0.815 0.780 0.780 0.829 0.819 0.819 bank 4521 16 2 0.985 0.980 0.981 0.981 0.982 0.983 bioconcentration 779 9 3 0.764 0.749 0.741 0.753 0.744 0.734 blood 748 4 2 0.902 0.884 0.905 0.901 0.902 0.904 breast-cancer 286 9 2 0.829 0.770 0.823 0.823 0.821 0.834 breast-cancer-wisc 699 9 2 0.998 0.995 0.995 0.998 0.998 0.997 breast-cancer-wisc-diag 569 30 2 0.997 0.995 0.997 0.998 0.996 0.998 breast-cancer-wisc-prog 198 33 2 0.875 0.855 0.833 0.858 0.859 0.860 breast-tissue 106 9 6 0.929 0.904 0.906 0.931 0.907 0.920 car 1728 6 4 0.997 0.998 0.998 0.999 1.000 0.999 cardiotocography-10clases 2126 21 10 0.960 0.954 0.952 0.962 0.956 0.955 cardiotocography-3clases 2126 21 3 0.990 0.988 0.989 0.992 0.991 0.992 chess-krvk 28056 6 18 0.923 0.919 0.912 0.806 0.753 0.738 chess-krvkp 3196 36 2 0.999 0.999 1.000 1.000 0.999 1.000 climate 540 20 2 0.988 0.991 0.993 0.987 0.989 0.990 congressional-voting 435 16 2 0.745 0.700 0.719 0.713 0.713 0.717 conn-bench-sonar-mines-rocks 208 60 2 0.939 0.948 0.952 0.954 0.951 0.956 conn-bench-vowel-deterding 990 11 11 0.998 0.994 0.990 0.998 0.996 0.996 connect-4 67557 42 2 0.975 0.975 0.975 0.959 0.954 0.954 contrac 1473 9 3 0.743 0.739 0.737 0.740 0.739 0.729 credit-approval 690 15 2 0.953 0.936 0.935 0.944 0.942 0.943 cylinder-bands 512 35 2 0.846 0.820 0.830 0.869 0.865 0.872 dermatology 366 34 6 0.998 0.996 0.999 1.000 1.000 0.999 echocardiogram 131 10 2 0.945 0.934 0.918 0.949 0.944 0.949 ecoli 336 7 8 0.958 0.954 0.963 0.956 0.955 0.959 energy-y1 768 8 3 0.997 0.978 0.979 0.996 0.993 0.993 energy-y2 768 8 3 0.993 0.975 0.973 0.990 0.986 0.987 fertility 100 9 2 0.957 0.917 0.921 0.960 0.928 0.933 flags 194 28 8 0.734 0.679 0.702 0.747 0.697 0.718 glass 214 9 6 0.832 0.729 0.741 0.833 0.786 0.824 haberman-survival 306 3 2 0.869 0.769 0.795 0.845 0.840 0.841 hayes-roth 160 3 3 0.973 0.969 0.962 0.954 0.901 0.903
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Dataset N M K RED+BNN BNN MCP BNN Entropy RED+MC-D MC-D MCP MC-D Entropy
heart-cleveland 303 13 5 0.891 0.879 0.884 0.865 0.863 0.866 heart-hungarian 294 12 2 0.933 0.913 0.927 0.924 0.904 0.908 heart-switzerland 123 12 5 0.562 0.505 0.489 0.470 0.445 0.437 heart-va 200 12 5 0.379 0.312 0.323 0.443 0.410 0.407 hepatitis 155 19 2 0.968 0.958 0.972 0.957 0.966 0.967 hill-valley 1212 100 2 0.517 0.504 0.519 0.546 0.542 0.538 horse-colic 368 25 2 0.926 0.894 0.909 0.904 0.895 0.901 ilpd-indian-liver 583 9 2 0.872 0.845 0.852 0.879 0.875 0.875 image-segmentation 2310 18 7 0.997 0.995 0.997 0.998 0.998 0.998 ionosphere 351 33 2 0.984 0.977 0.982 0.991 0.991 0.991 iris 150 4 3 0.997 0.994 0.993 0.997 0.996 0.997 led-display 1000 7 10 0.886 0.881 0.870 0.887 0.884 0.878 lenses 24 4 3 0.860 0.783 0.798 0.988 0.934 0.922 letter 20000 16 26 0.996 0.997 0.997 0.988 0.985 0.980 libras 360 90 15 0.878 0.845 0.728 0.952 0.938 0.924 low-res-spect 531 100 9 0.973 0.973 0.979 0.978 0.978 0.984 lung-cancer 32 56 3 0.645 0.613 0.628 0.666 0.582 0.519 lymphography 148 18 4 0.948 0.941 0.942 0.929 0.934 0.932 magic 19020 10 2 0.969 0.968 0.968 0.968 0.967 0.967 mammographic 961 5 2 0.924 0.908 0.915 0.921 0.918 0.919 messidor 1151 19 2 0.811 0.808 0.809 0.871 0.871 0.870 miniboone 130064 50 2 0.993 0.992 0.993 0.992 0.992 0.993 molec-biol-promoter 106 57 2 0.932 0.896 0.904 0.925 0.897 0.897 molec-biol-splice 3190 60 3 0.977 0.976 0.975 0.970 0.970 0.972 monks-1 556 6 2 0.999 0.996 0.997 1.000 0.999 0.999 monks-2 601 6 2 0.964 0.808 0.816 0.870 0.821 0.820 monks-3 554 6 2 0.997 0.996 0.996 0.999 0.997 0.997 mushroom 8124 21 2 NA NA NA NA NA NA musk-1 476 166 2 0.937 0.914 0.918 0.980 0.979 0.980 musk-2 6598 166 2 0.999 0.998 0.998 1.000 0.999 1.000 nursery 12960 8 5 1.000 1.000 1.000 1.000 1.000 1.000 oocytes merluccius nucleus 4d 1022 41 2 0.955 0.779 0.780 0.942 0.936 0.939 oocytes merluccius states 2f 1022 25 3 0.987 0.984 0.985 0.991 0.989 0.991 oocytes trisopterus nucleus 2f 912 25 2 0.914 0.849 0.866 0.936 0.925 0.933 oocytes trisopterus states 5b 912 32 3 0.984 0.977 0.980 0.993 0.992 0.993 optical 5620 62 10 0.997 0.998 0.999 0.999 0.998 0.999 ozone 2536 72 2 0.996 0.995 0.995 0.995 0.995 0.996 page-blocks 5473 10 5 0.998 0.997 0.998 0.998 0.998 0.998 parkinsons 195 22 2 0.988 0.976 0.977 0.985 0.987 0.987 pendigits 10992 16 10 0.999 0.999 0.999 0.999 0.999 0.999 phishing 1353 9 3 0.974 0.967 0.967 0.979 0.975 0.974 pima 768 8 2 0.896 0.889 0.906 0.900 0.898 0.903 pittsburg-bridges-MATERIAL 106 7 3 0.977 0.964 0.969 0.975 0.973 0.972 pittsburg-bridges-REL-L 103 7 3 0.798 0.756 0.709 0.784 0.771 0.775 pittsburg-bridges-SPAN 92 7 3 0.845 0.835 0.822 0.843 0.794 0.799 pittsburg-bridges-T-OR-D 102 7 2 0.954 0.913 0.933 0.964 0.941 0.940 pittsburg-bridges-TYPE 105 7 6 0.785 0.691 0.744 0.765 0.697 0.683 planning 182 12 2 0.766 0.750 0.765 0.699 0.692 0.682 plant-margin 1600 64 100 0.935 0.933 0.908 0.954 0.952 0.919 plant-shape 1600 64 100 0.713 0.683 0.620 0.874 0.856 0.804 plant-texture 1599 64 100 0.947 0.945 0.921 0.947 0.940 0.902 post-operative 90 8 3 0.659 0.633 0.635 0.667 0.663 0.662 primary-tumor 330 17 15 0.777 0.740 0.747 0.753 0.744 0.747 ringnorm 7400 20 2 0.999 0.999 0.999 0.998 0.997 0.998 seeds 210 7 3 0.980 0.984 0.990 0.990 0.985 0.985 semeion 1593 256 10 0.992 0.992 0.992 0.992 0.993 0.993 soybean 683 35 18 0.996 0.994 0.993 0.996 0.996 0.992 spambase 4601 57 2 0.987 0.986 0.990 0.989 0.989 0.992 spect 265 22 2 0.769 0.743 0.769 0.785 0.772 0.773 spectf 267 44 2 0.965 0.956 0.954 0.958 0.951 0.949 statlog-australian-credit 690 14 2 0.739 0.663 0.659 0.716 0.700 0.700 statlog-german-credit 1000 24 2 0.904 0.899 0.899 0.891 0.897 0.898 statlog-heart 270 13 2 0.936 0.928 0.929 0.931 0.924 0.930 statlog-image 2310 18 7 0.998 0.997 0.997 0.998 0.998 0.998 statlog-landsat 6435 36 6 0.984 0.982 0.981 0.985 0.986 0.985 statlog-shuttle 58000 9 7 1.000 0.999 1.000 1.000 1.000 1.000 statlog-vehicle 846 18 4 0.944 0.940 0.914 0.970 0.967 0.964 steel-plates 1941 27 7 0.923 0.912 0.900 0.923 0.913 0.916 synthetic-control 600 60 6 0.998 0.997 1.000 1.000 0.999 0.999 teaching 151 5 3 0.589 0.555 0.517 0.694 0.627 0.618 thyroid 7200 21 3 1.000 0.999 1.000 0.999 0.999 1.000 tic-tac-toe 958 9 2 1.000 0.998 0.999 1.000 0.999 0.999 titanic 2201 3 2 0.890 0.884 0.885 0.890 0.889 0.891 trains 10 29 2 1.000 0.750 0.875 0.969 0.719 0.719 twonorm 7400 20 2 0.999 0.999 0.999 0.999 0.998 0.999 vertebral-column-2clases 310 6 2 0.975 0.964 0.967 0.976 0.975 0.977 vertebral-column-3clases 310 6 3 0.971 0.967 0.965 0.976 0.975 0.975
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Dataset N M K RED+BNN BNN MCP BNN Entropy RED+MC-D MC-D MCP MC-D Entropy
wall-following 5456 24 4 0.990 0.987 0.990 0.992 0.992 0.994 waveform 5000 21 3 0.973 0.970 0.970 0.973 0.973 0.972 waveform-noise 5000 40 3 0.975 0.974 0.973 0.971 0.971 0.971 wine 178 13 3 0.997 0.994 0.999 0.999 0.998 0.999 wine-quality-red 1599 11 6 0.725 0.697 0.711 0.736 0.728 0.725 wine-quality-white 4898 11 7 0.639 0.624 0.607 0.664 0.653 0.634 yeast 1484 8 10 0.721 0.716 0.715 0.747 0.741 0.718 zoo 101 16 7 0.997 0.996 0.997 0.998 0.996 0.995
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Table 14: Comparison between RED and Counterparts Using AUROC
Dataset N M K RED+BNN BNN MCP BNN Entropy RED+MC-D MC-D MCP MC-D Entropy
abalone 4177 8 3 0.731 0.727 0.718 0.725 0.723 0.716 acute-inflammation 120 6 2 NA NA NA NA NA NA acute-nephritis 120 6 2 NA NA NA NA NA NA adult 48842 14 2 0.845 0.842 0.843 0.846 0.841 0.841 annealing 898 31 5 0.928 0.798 0.764 0.883 0.850 0.836 arrhythmia 452 262 13 0.766 0.703 0.784 0.709 0.698 0.700 audiology-std 196 59 18 0.850 0.814 0.865 0.874 0.814 0.851 balance-scale 625 4 3 0.982 0.946 0.965 0.977 0.955 0.968 balloons 16 4 2 0.762 0.714 0.714 0.778 0.722 0.722 bank 4521 16 2 0.900 0.867 0.876 0.868 0.872 0.877 bioconcentration 779 9 3 0.669 0.636 0.639 0.619 0.609 0.586 blood 748 4 2 0.757 0.723 0.753 0.731 0.731 0.735 breast-cancer 286 9 2 0.662 0.581 0.652 0.672 0.668 0.678 breast-cancer-wisc 699 9 2 0.944 0.876 0.911 0.943 0.938 0.936 breast-cancer-wisc-diag 569 30 2 0.898 0.769 0.896 0.941 0.893 0.929 breast-cancer-wisc-prog 198 33 2 0.720 0.687 0.671 0.696 0.692 0.689 breast-tissue 106 9 6 0.891 0.860 0.862 0.909 0.883 0.888 car 1728 6 4 0.964 0.956 0.942 0.977 0.977 0.965 cardiotocography-10clases 2126 21 10 0.848 0.835 0.810 0.845 0.831 0.818 cardiotocography-3clases 2126 21 3 0.912 0.896 0.901 0.913 0.905 0.910 chess-krvk 28056 6 18 0.792 0.781 0.762 0.777 0.716 0.696 chess-krvkp 3196 36 2 0.917 0.883 0.954 0.968 0.927 0.956 climate 540 20 2 0.906 0.877 0.916 0.881 0.868 0.897 congressional-voting 435 16 2 0.632 0.594 0.605 0.616 0.615 0.617 conn-bench-sonar-mines-rocks 208 60 2 0.780 0.794 0.825 0.827 0.824 0.836 conn-bench-vowel-deterding 990 11 11 0.965 0.916 0.863 0.969 0.936 0.928 connect-4 67557 42 2 0.852 0.851 0.851 0.832 0.807 0.808 contrac 1473 9 3 0.691 0.690 0.683 0.679 0.677 0.665 credit-approval 690 15 2 0.773 0.723 0.735 0.753 0.750 0.753 cylinder-bands 512 35 2 0.705 0.643 0.653 0.702 0.700 0.705 dermatology 366 34 6 0.937 0.882 0.969 0.988 0.987 0.982 echocardiogram 131 10 2 0.737 0.703 0.719 0.798 0.776 0.786 ecoli 336 7 8 0.817 0.807 0.835 0.780 0.777 0.800 energy-y1 768 8 3 0.980 0.849 0.849 0.933 0.888 0.888 energy-y2 768 8 3 0.952 0.840 0.821 0.921 0.889 0.894 fertility 100 9 2 0.594 0.439 0.471 0.707 0.609 0.608 flags 194 28 8 0.756 0.721 0.730 0.752 0.699 0.715 glass 214 9 6 0.739 0.578 0.589 0.668 0.612 0.660 haberman-survival 306 3 2 0.712 0.544 0.559 0.700 0.699 0.703 hayes-roth 160 3 3 0.888 0.863 0.837 0.880 0.786 0.787 heart-cleveland 303 13 5 0.838 0.827 0.834 0.795 0.795 0.800 heart-hungarian 294 12 2 0.785 0.746 0.766 0.743 0.718 0.728 heart-switzerland 123 12 5 0.637 0.592 0.543 0.521 0.487 0.444 heart-va 200 12 5 0.627 0.568 0.590 0.583 0.545 0.561 hepatitis 155 19 2 0.853 0.812 0.859 0.843 0.849 0.850 hill-valley 1212 100 2 0.511 0.495 0.528 0.560 0.559 0.543 horse-colic 368 25 2 0.733 0.664 0.688 0.702 0.693 0.708 ilpd-indian-liver 583 9 2 0.723 0.655 0.671 0.723 0.716 0.717 image-segmentation 2310 18 7 0.934 0.898 0.938 0.946 0.936 0.952 ionosphere 351 33 2 0.853 0.777 0.851 0.882 0.874 0.878 iris 150 4 3 0.975 0.931 0.908 0.961 0.953 0.961 led-display 1000 7 10 0.760 0.753 0.728 0.760 0.756 0.738 lenses 24 4 3 0.798 0.667 0.702 0.979 0.885 0.865 letter 20000 16 26 0.940 0.950 0.943 0.935 0.920 0.895 libras 360 90 15 0.804 0.739 0.594 0.818 0.767 0.720 low-res-spect 531 100 9 0.873 0.859 0.885 0.862 0.853 0.885 lung-cancer 32 56 3 0.669 0.601 0.596 0.722 0.613 0.564 lymphography 148 18 4 0.770 0.741 0.742 0.767 0.758 0.748 magic 19020 10 2 0.816 0.814 0.816 0.813 0.811 0.811 mammographic 961 5 2 0.753 0.739 0.740 0.757 0.754 0.755 messidor 1151 19 2 0.661 0.655 0.648 0.691 0.689 0.686
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Dataset N M K RED+BNN BNN MCP BNN Entropy RED+MC-D MC-D MCP MC-D Entropy
miniboone 130064 50 2 0.906 0.903 0.904 0.905 0.904 0.905 molec-biol-promoter 106 57 2 0.803 0.676 0.745 0.779 0.720 0.713 molec-biol-splice 3190 60 3 0.858 0.853 0.845 0.851 0.839 0.849 monks-1 556 6 2 0.989 0.877 0.918 0.991 0.963 0.975 monks-2 601 6 2 0.934 0.581 0.611 0.742 0.639 0.641 monks-3 554 6 2 0.959 0.930 0.934 0.970 0.950 0.950 mushroom 8124 21 2 NA NA NA NA NA NA musk-1 476 166 2 0.838 0.769 0.792 0.835 0.821 0.828 musk-2 6598 166 2 0.968 0.957 0.961 0.971 0.937 0.971 nursery 12960 8 5 0.957 0.965 0.974 0.994 0.968 0.967 oocytes merluccius nucleus 4d 1022 41 2 0.910 0.649 0.641 0.771 0.761 0.764 oocytes merluccius states 2f 1022 25 3 0.891 0.873 0.872 0.902 0.892 0.905 oocytes trisopterus nucleus 2f 912 25 2 0.793 0.677 0.697 0.752 0.735 0.750 oocytes trisopterus states 5b 912 32 3 0.875 0.832 0.854 0.903 0.898 0.914 optical 5620 62 10 0.897 0.907 0.963 0.949 0.941 0.960 ozone 2536 72 2 0.899 0.866 0.882 0.889 0.872 0.909 page-blocks 5473 10 5 0.945 0.932 0.947 0.948 0.942 0.950 parkinsons 195 22 2 0.932 0.851 0.865 0.890 0.869 0.873 pendigits 10992 16 10 0.936 0.934 0.947 0.946 0.937 0.941 phishing 1353 9 3 0.884 0.841 0.840 0.876 0.863 0.855 pima 768 8 2 0.755 0.750 0.752 0.751 0.750 0.750 pittsburg-bridges-MATERIAL 106 7 3 0.839 0.775 0.797 0.833 0.816 0.813 pittsburg-bridges-REL-L 103 7 3 0.682 0.625 0.576 0.656 0.624 0.629 pittsburg-bridges-SPAN 92 7 3 0.716 0.698 0.702 0.675 0.618 0.643 pittsburg-bridges-T-OR-D 102 7 2 0.793 0.715 0.742 0.780 0.730 0.740 pittsburg-bridges-TYPE 105 7 6 0.791 0.728 0.750 0.771 0.715 0.712 planning 182 12 2 0.518 0.496 0.519 0.452 0.446 0.441 plant-margin 1600 64 100 0.835 0.829 0.767 0.855 0.850 0.768 plant-shape 1600 64 100 0.759 0.730 0.677 0.801 0.773 0.706 plant-texture 1599 64 100 0.856 0.853 0.796 0.853 0.840 0.760 post-operative 90 8 3 0.380 0.364 0.357 0.428 0.415 0.414 primary-tumor 330 17 15 0.831 0.811 0.803 0.781 0.777 0.777 ringnorm 7400 20 2 0.953 0.943 0.952 0.940 0.915 0.946 seeds 210 7 3 0.875 0.869 0.932 0.929 0.896 0.902 semeion 1593 256 10 0.916 0.915 0.903 0.913 0.917 0.914 soybean 683 35 18 0.932 0.907 0.901 0.950 0.948 0.904 spambase 4601 57 2 0.853 0.847 0.873 0.861 0.858 0.889 spect 265 22 2 0.616 0.576 0.592 0.656 0.646 0.649 spectf 267 44 2 0.895 0.845 0.848 0.777 0.751 0.744 statlog-australian-credit 690 14 2 0.628 0.502 0.490 0.582 0.557 0.561 statlog-german-credit 1000 24 2 0.793 0.781 0.786 0.717 0.720 0.722 statlog-heart 270 13 2 0.739 0.701 0.703 0.736 0.712 0.724 statlog-image 2310 18 7 0.949 0.938 0.938 0.953 0.947 0.954 statlog-landsat 6435 36 6 0.890 0.876 0.874 0.884 0.881 0.877 statlog-shuttle 58000 9 7 0.999 0.823 0.998 0.999 0.905 0.998 statlog-vehicle 846 18 4 0.829 0.818 0.741 0.861 0.857 0.837 steel-plates 1941 27 7 0.824 0.788 0.757 0.802 0.786 0.773 synthetic-control 600 60 6 0.861 0.738 0.980 0.974 0.965 0.966 teaching 151 5 3 0.656 0.618 0.614 0.690 0.624 0.611 thyroid 7200 21 3 0.977 0.950 0.985 0.968 0.951 0.984 tic-tac-toe 958 9 2 0.990 0.875 0.961 0.985 0.951 0.975 titanic 2201 3 2 0.677 0.664 0.665 0.669 0.668 0.674 trains 10 29 2 1.000 0.667 0.833 0.958 0.625 0.625 twonorm 7400 20 2 0.958 0.956 0.957 0.950 0.945 0.955 vertebral-column-2clases 310 6 2 0.871 0.836 0.846 0.851 0.847 0.858 vertebral-column-3clases 310 6 3 0.888 0.877 0.870 0.861 0.860 0.859 wall-following 5456 24 4 0.894 0.879 0.889 0.909 0.900 0.919 waveform 5000 21 3 0.845 0.834 0.833 0.850 0.849 0.845 waveform-noise 5000 40 3 0.858 0.856 0.849 0.850 0.850 0.849 wine 178 13 3 0.923 0.857 0.976 0.979 0.949 0.954 wine-quality-red 1599 11 6 0.657 0.629 0.622 0.640 0.626 0.611 wine-quality-white 4898 11 7 0.583 0.568 0.544 0.598 0.588 0.560 yeast 1484 8 10 0.675 0.667 0.648 0.690 0.687 0.645 zoo 101 16 7 0.980 0.966 0.965 0.989 0.967 0.963
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Review summaries:
This paper introduces RED, a new methodology to produce reliable confidence scores to detect missclassification errors in neural networks. The idea is to combine kernels based on both input and output spaces (as in RIO) to define a (sparse) GP that estimates the residual between the correctness of the original prediction and the maximum class probability. The authors show enhanced performance against other related methods and the ability of RED to detect OOD and adversarial data through the variance of the confidence score. 

In this paper, their goal is to improve calibration and accuracy by augmenting a classification model with a GP. They base their model off RIO (ICLR 2020) which targets regression problems and tries to predict the residual between predicted value and true value. They propose a model, RED, which instead tries to predict the residual between the predicted confidence score for the true class and 1 — the true class target confidence score using a GP. They show strong improvements over the methods they compare to for 125 UCI datasets and CIFAR-10 dataset.

This paper solves an interesting problem of predicting uncertainty in NN without re-raining/modifying the existing NN. The authors propose a framework to calculate a confidence score for detecting misclassification errors by calibrating the NN classifier’s confidence scores and estimates uncertainty around the calibrated scores using Gaussian processes. This framework is called RED (Residual i/o Error Detection).