Feature attribution methods identify "relevant" features as an explanation of a complex machine learning model. Several feature attribution methods have been proposed; however, only a few studies have attempted to define the "relevance" of each feature mathematically. In this study, we formalize the feature attribution problem as a feature selection problem. In our proposed formalization, there arise two possible definitions of relevance. We name the feature attribution problems based on these two relevances as Exclusive Feature Selection (EFS) and Inclusive Feature Selection (IFS). We show that several existing feature attribution methods can be interpreted as approximation algorithms for EFS and IFS. Moreover, through exhaustive experiments, we show that IFS is better suited as the formalization for the feature attribution problem than EFS.
This paper formulates feature attribution from a feature selection perspective, and compares EFS (Exclusive Feature Selection) and IFS (Exclusive Feature Selection), which shows IFS is a better fit for feature attribution.
[+] The paper is well-structured and the proposed approach is clearly presented.
[-] It would helpful if the author could discuss the time complexity of proposed methods and compare the running time with baseline methods in evaluation.
[-] My major concern on this paper is the significance, as the contribution of the paper seems to be very limited.
    1) Formalizing the feature attribution problem as a feature selection problem is straightforward. IFS and EFS are just Forward and Backward stepwise feature selection, which are classic feature selection schemes. Applying them to feature attribution/saliency map does not seem to have much technical contribution.
    2) One claimed contribution of this paper is that existing feature attribution methods can be viewed as approximation of IFS and EFS. However, this contribution also seems to be minor. As many feature selection methods are known to be approximation of backward or forward stepwise feature selection, it is straightforward to show the connection between other feature attribution methods and IFS/EFS.
In conclusion, I would recommend to reject this paper due to the limited novelty and technical contribution.