ICFI: a Feature Importance Measure For Multi-Class Classification

Authors: Tommaso Amico, Pernille Matthews, Ira Assent
Primary area: interpretability and explainable AI
Keywords: feature importance, explainable artificial intelligence, multi-class classification

Abstract:
Feature importance is one of the most prominent methods in explainable artificial intelligence. It seeks to score the features an artificial intelligence model relies on the most. In multi-class classification, current methods fail to explain inter-class relationships as they either provide explanations for binary classification only, or suffer from aggregation bias. In a multi-class classification scenario, features may carry discriminative power to separate some of the classes while being otherwise less relevant. State-of-the-art feature importance measures do not capture this behavior. We propose Inter-Class Feature Importance (ICFI), a measure that scores the feature importance to discriminate between an arbitrary pair of classes. ICFI is a post-hoc, model-agnostic method, independent from the machine learning architecture employed. ICFI marginalises the target output with respect to the feature of interest, leveraging the resulting change in model behavior to quantify feature importance. We present ICFI’s properties and argue its relevance, describing use cases and showing insights gained. We demonstrate through thorough experiments on real-world datasets how ICFI captures the features characteristics for specific class relationships.

Decision: reject
Reviewer scores: 5, 5, 5

Reviews and discussion:
<thread>
creation date:
1729995082894

thread_type:
official_review

<thread_object>
creator: reviewer_u1uf

<content>
summary:
The paper introduces Inter-Class Feature Importance (ICFI), a model agnostic method to identify important features to distinguish between a pair of classes for multi-class classification tasks. ICFI is a permutation based feature selection method that measures the drop/increase in model performance when merging a pair of classes. Intuitively if permutations in values of a feature results in bigger model performance deterioration when merging two classes, the feature plays a more important role in distinguishing between those two classes.
Authors study the feature importance from ICFI for various class pairs for a couple of commonly used data sets (Iris and 20 news group) and compare them against a global method (i.e. Permutation Feature Importance). They also, study test accuracy based on number of features used in training a multi-class classifier over four data sets when features are selected based on ICFI and three alternative methods, i.e. PFI, Shapley additive explanations (SHAP), and a random feature selector and show that ICFI outperforms other methods in their studies.

strengths:
- The paper has done a decent job covering related literature.
- The idea of merging classes, permuting a feature and measuring the delta in empirical error makes sense and is well explained.
- The XAI problem and in particular identifying important features for multi-class classification tasks by looking into features that can help distinguishing between a given pair of classes is a worthwhile problem to solve witch wide use cases.

weaknesses:
- The biggest weakness of the method is the lack of clarity in the proposed method. It is not clear how the top important features identified by ICFI for various pairs of classes are combined to train a multi-class classifier. For example, in Fig. 7, test accuracy is measured at different numbers of selected features and it is not explained how important features identified by ICFI across various pairs of classes are being selected.
- Along the same lines of the first point, it is not clear if ICFI is expected to provide explainability at global level or in terms of distinguishing between a pair of classes only. If the goal is the former, then authors should have discussed more details about how they combine feature importance of various pairs of classes and come up with a global ranking. If it is the latter, then it's not clear why they compare ICFI with global methods such as FPI (e.g. in Fig. 4).
- Some of the fundamental assumptions/claims of the paper needs further clarification. For example, in the abstract section, authors state that "In multi-class classification, current methods fail to explain inter-class relationships as they either provide explanations for binary classification only, or suffer from aggregation bias", however, there are methods such as LIME for which none of the listed limitations applies.
- Along the previous point, the alternative methods studied in the paper, do not include methods such as LIME that theoretically do not suffer from the stated limitations for existing methods.
- Lastly, the limitations of ICFI are not discussed in the paper. For example, how can ICFI capture the interactions between two or more features, as a concrete case consider a couple of features that do not show strong performance gain on their own while their combination could play a significant role in the predictive power of the model.

questions:
- I can imagine that for the 20 news group data set, since features are words, permuting a feature means replacing it with an unused token/word in the vocabulary (e.g. NULL)? It would be useful if authors can clarify.
- Other questions are captured in the first two bullets of the weakness box.
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</thread>
<thread>
creation date:
1730185190986

thread_type:
official_review

<thread_object>
creator: reviewer_bfbc

<content>
summary:
This paper focuses on the problem that in the multi-class classification scenario, current methods fail to explain inter-class relationships as they either provide explanations for binary classification only or suffer from aggregation bias. In a multi-class classification scenario, features may carry discriminative power to separate some of the classes while being otherwise less relevant. Focusing on this problem, the authors proposed an Inter-Class Feature Importance (ICFI), to score the feature's importance between an arbitrary pair of classes.

strengths:
This paper presents an "Inter-Class Feature Importance (ICFI)" method, specifically addressing the problem of inter-class feature importance in multi-class classification, filling the gap in the measurement of inter-class relationship in existing methods. The method can provide model-agnostic explanations without the need to train additional models, addressing the limitations of existing methods in measuring feature importance in multi-class scenario.

weaknesses:
(1)The paper lacks enough innovation. ICFI avoids the aggregation bias commonly seen in traditional global feature importance methods by introducing a feature importance assessment based on class pairs and feature permuting. At present, some relevant studies have focused on feature permuting and class pairs, such as PFI, SHAP, and LOO. The ICFI method proposed in this paper mainly evaluates the importance of features through feature permuting, and its method mechanism is similar to PFI and SHAP. Therefore, although ICFI has some potential contribution to multi-class feature importance assessment, it is not innovative enough to clearly distinguish ICFI from existing methods.

(2) The writing of this paper should be further improved. There are many writing problems in the paper, which seriously affects the reading. The following are some of the problems I found:
--- On page 2, line 67, "a ML" should be "an ML". On page 2, line 83 has the same error.
--- "Fig 3", and "Figure 3" all appear in this paper. Ensure consistent figure references.
--- On page 3, line 152, "Fig. 8 shows the decision...". "Fig. 8" should be Fig. 1.
--- On page 4, line 205, the symbol "D" lacks definition.
--- On page 8, line 395, "Figure 8 shows the global PFI feature importance...", "Fig. 8" should be Fig. 4.
--- On page 10, in the Fig. 7, the "Shap" should be consistent with "SHAP" in this paper.
--- The description of the figure title is too complicated in this paper, so it is recommended to simplify the figure title to improve readability and add a serial number to the sub-graph for easy reference and understanding.

questions:
In addition to the weaknesses I have already listed, I have the following questions:
(1)In Section 4.3, four different datasets were selected to verify the performance of ICFI in different tasks, but the complexity of these datasets is limited, and it is recommended to further extend the experiment to complex datasets with high dimensional and larger number of categories.

(2)The idea of assessing feature importance through feature permuting of ICFI is similar to that of PFI. Please analyze the uniqueness and advantages of ICFI similarly.

(3)In the Section 4.3, ICFI was compared to SHAP, PFI, and random strategies, why not choose LIME and PDP as the comparison algorithms?

(4)In this paper, the One-vs-One strategy is used to train the model and perform majority voting, but this method may incur high computational costs when the number of categories is high. It is suggested that the authors add an analysis of the computational efficiency of ICFI .
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</thread>
<thread>
creation date:
1730670324819

thread_type:
official_review

<thread_object>
creator: reviewer_h9xb

<content>
summary:
This submission proposes Inter-Class Feature Importance (ICFI) measure, which scores the feature importance to discriminate between pairs of classes in multi-class classification (MCC). The measure is formulated in Section 3.1, and experiments are conducted in Section 4 to assess the potential usefulness of the proposed measure. 

The notion of ICFI, which is compactly described in Eq (4), is interesting (at least from the conceptual level). However, it seems that the scalability with respect to both the number of features and classes is a crucial challenge in practice.

strengths:
S1: I think the notion of ICFI is intuitive and interesting. 

S2: Empirical results seem to be in favor of the proposed ICFI measure.

weaknesses:
W1:  It seems that the scalability with respect to both the number of features and classes is a crucial challenge in practice.

W2:  It might be beneficial to discuss how ICFI can be adapted to enlarge the existing set of instance-level feature importance measures.

questions:
Q/S1: For each pair of classes, for each feature of interest, did you train one model (after permuting the corresponding feature of interest)? 

Q/S2:  Could you comment on the scalability of the proposed ICFI measure? How many features and classes one might handle by using the proposed ICFI measure? Can it be scalable up to image data sets? 

Q/S3: Could you discuss how ICFI can be adapted to enlarge the existing set of instance-level feature importance measures?
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</thread>
<thread>
creation date:
1731879297461

thread_type:
official_comment

<thread_object>
creator: authors

<content>
comment:
We thank the reviewers for the detailed feedback provided and we appreciate that ICFI is seen as intuitive, interesting, filling a gap in the literature with a reasonable and well-explained merging idea.
We appreciate the comments for further improving our paper. In particular, we revise our description of the evaluation using model re-training, leveraging a One vs One strategy, to emphasise that is an assessment strategy only.

The One vs One strategy is not utilised as part of computing ICFI. Our proposed measure is model-agnostic and can, therefore, be applied on top of any black box model and does not need to be able to calculate a One vs One strategy, with the limitations this would have as rightfully pointed out by the reviewers. 
In Section 4.3, the One vs One strategy is leveraged to assess the quality of feature rankings and to benchmark ICFI against other ranking methods. Because of the lack of competing methods, we demonstrate the benefit of our novel ICFI method by benchmarking against global methods and a baseline with a random strategy.
Our evaluation approach in Section 4.3 starts by training a black-box model. We then compute global SHAP, PFI, LIME (introduced in the revised version of the paper) along with ICFI and a random feature selection for each class combination. Among these, ICFI and the random strategy are the only methods that produce different rankings for each class pair, whereas global methods provide a single ranking across all class pairs.
We continue by training a binary classifier for each pair of classes, aggregating decisions through majority voting (One vs One). This process involves selecting only the top k features, considering multiple values for k. When retraining based on ICFI rankings, different features for each class pair are selected, whereas global methods use the same feature selection, as they lack class-pair-specific rankings.
Our experiments demonstrate that the features deemed most important globally are not necessarily the most important for distinguishing between specific pairs of classes. Feature selection driven by global methods results in lower performance compared to ICFI, as the aggregation inherent in global approaches obscures valuable insights about individual class pairs, leading to aggregation bias.
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Metareview:
metareview:
The paper introduces a new measure for feature importance suited for multi-classification problems. The measure focuses on intra-class relationships by scoring features to discriminate between an arbitrary pair of classes. This behavior is ignored by current state-of-the-art approaches.   

The Reviewers state that the method is interesting, intuitive, and important in the context of explainable artificial intelligence. They underline that the method is model-agnostic. The empirical results are promising. The Authors extensively present the related work. Nevertheless, the Reviewers remarked that the paper is not entire clear, for example, the method, its usage and its purpose. The initial version of the paper was lacking a comparison to an important related method. 

Overall, the contribution seems to be interesting, but not good enough for a top ML conference. I encourage the Authors to improve clarify of the paper to avoid any confusions around the method and the experimental studies/use cases.