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Title: Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams

Paper content:
In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget.
In this paper, we propose a novel approach for cost-sensitive feature acquisition at the prediction-time.
The suggested method acquires features incrementally based on a context-aware feature-value function.
We formulate the problem in the reinforcement learning paradigm, and introduce a reward function based on the utility of each feature.
Specifically, MC dropout sampling is used to measure expected variations of the model uncertainty which is used as a feature-value function.
Furthermore, we suggest sharing representations between the class predictor and value function estimator networks.
The suggested approach is completely online and is readily applicable to stream learning setups.
The solution is evaluated on three different datasets including the well-known MNIST dataset as a benchmark as well as two cost-sensitive datasets: Yahoo Learning to Rank and a dataset in the medical domain for diabetes classification.
According to the results, the proposed method is able to efficiently acquire features and make accurate predictions.
In traditional machine learning settings, it is usually assumed that a training dataset is freely available and the objective is to train models that generalize well.
In this paradigm, the feature set is fixed, and we are dealing with complete feature vectors accompanied by class labels that are provided for training.
However, in many real-world scenarios, there are certain costs for acquiring features as well as budgets limiting the total expenditure.
Here, the notation of cost is more general than financial cost and it also refers to other concepts such as computational cost, privacy impacts, energy consumption, patient discomfort in medical tests, and so forth BID22 .
Take the example of the disease diagnosis based on medical tests.
Creating a complete feature vector from all the relevant information is synonymous with conducting many tests such as MRI scan, blood test, etc. which would not be practical.
On the other hand, a physician approaches the problem by asking a set of basic easy-to-acquire features, and then incrementally prescribes other tests based on the current known information (i.e., context) until a reliable diagnosis can be made.
Furthermore, in many real-world use-cases, due to the volume of data or necessity of prompt decisions, learning and prediction should take place in an online and stream-based fashion.
In the medical diagnosis example, it is consistent with the fact that the latency of diagnosis is vital (e.g., urgency of specific cases and diagnosis), and it is often impossible to defer the decisions.
Here, by online we mean processing samples one at a time as they are being received.Various approaches were suggested in the literature for cost-sensitive feature acquisition.
To begin with, traditional feature selection methods suggested to limit the set of features being used for training BID11 BID17 .
For instance, L1 regularization for linear classifiers results in models that effectively use a subset of features BID9 .
Note that these methods focus on finding a fixed subset of features to be used (i.e., feature selection), while a more optimal solution would be making feature acquisition decisions based on the sample at hand and at the prediction-time.More recently, probabilistic methods were suggested that measure the value of each feature based on the current evidence BID5 .
However, these methods are usually applicable to Bayesian networks or similar probabilistic models and make limiting assumptions such as having binary features and binary classes .
Furthermore, these probabilistic methods are computationally expensive and intractable in large scale problems BID5 .Motivated
by the success of discriminative learning, cascade and tree based classifiers suggested as an intuitive way to incorporate feature costs BID20 BID3 . Nevertheless
, these methods are basically limited to the modeling capability of tree classifiers and are limited to fixed predetermined structures. A recent work
by BID27 suggested a gating method that employs adaptive linear or tree-based classifiers, alternating between low-cost models for easy-to-handle instances and higher-cost models to handle more complicated cases. While this method
outperforms many of the previous work on the tree-based and cascade cost-sensitive classifiers, the low-cost model being used is limited to simple linear classifiers or pruned random forests.As an alternative approach, sensitivity analysis of trained predictors is suggested to measure the importance of each feature given a context BID7 BID18 . These approaches
either require an exhaustive measurement of sensitivities or rely on approximations of sensitivity. These methods are
easy to use as they work without any significant modification to the predictor models being trained. However, theoretically
, finding the global sensitivity is a difficult and computationally expensive problem. Therefore, frequently
, approximate or local sensitivities are being used in these methods which may cause not optimal solutions.Another approach that is suggested in the literature is modeling the feature acquisition problem as a learning problem in the imitation learning BID13 or reinforcement learning BID14 BID29 BID15 domain. These approaches are
promising in terms of performance and scalability. However, the value functions
used in these methods are usually not intuitive and require tuning hyper-parameters to balance the cost vs. accuracy trade-off. More specifically, they often
rely on one or more hyper-parameters to adjust the average cost at which these models operate. On the other hand, in many real-world
scenarios it is desirable to adjust the trade-off at the prediction-time rather than the training-time. For instance, it might be desirable to
spend more for a certain instance or continue the feature acquisition until a desired level of prediction confidence is achieved. This paper presents a novel method based
on deep Q-networks for cost-sensitive feature acquisition. The proposed solution employs uncertainty
analysis in neural network classifiers as a measure for finding the value of each feature given a context. Specifically, we use variations in the certainty
of predictions as a reward function to measure the value per unit of the cost given the current context. In contrast to the recent feature acquisition methods
that use reinforcement learning ideas BID14 BID29 BID15 , the suggested reward function does not require any hyper-parameter tuning to balance cost versus performance trade-off. Here, features are acquired incrementally, while maintaining
a certain budget or a stopping criterion. Moreover, in contrast to many other work in the literature that
assume an initial complete dataset BID13 BID5 BID8 BID27 , the proposed solution is stream-based and online which learns and optimizes acquisition costs during the training and the prediction. This might be beneficial as, in many real-world use cases, it might
be prohibitively expensive to collect all features for all training data. Furthermore, this paper suggests a method for sharing the representations
between the class predictor and action-value models that increases the training efficiency.
In this paper, we proposed an approach for cost-sensitive learning in stream-based settings.
We demonstrated that certainty estimation in neural network classifiers can be used as a viable measure for the value of features.
Specifically, variations of the model certainty per unit of the cost is used as measure of feature value.
In this paradigm, a reinforcement learning solution is suggested which is efficient to train using a shared representation.
The introduced method is evaluated on three different real-world datasets representing different applications: MNIST digits recognition, Yahoo LTRC web ranking dataset, and diabetes prediction using health records.
Based on the results, the suggested method is able to learn from data streams, make accurate predictions, and effectively reduce the prediction-time feature acquisition cost.

assistant: text: An online algorithm for cost-aware feature acquisition and prediction