C-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation

Document Type

Conference Proceeding

Publication Date

1-1-2021

Abstract

In many image-classification applications, understanding the reasons of model's prediction can be as critical as the prediction's accuracy itself. Various feature-based local explainers have been designed to provide explanations on the decision of complex classifiers. Nevertheless, there is no consensus on evaluating the quality of different explanations. In response to this lack of comprehensive evaluation, we introduce the c-Eval metric and its corresponding framework to quantify the feature-based local explanation's quality. Given a classifier's prediction and the corresponding explanation on that prediction, c-Eval is the minimum-distortion perturbation that successfully alters the prediction while keeping the explanation's features unchanged. To show that c-Eval captures the importance of input's features, we establish a connection between c-Eval and the features returned by explainers in affine and nearly-affine classifiers. We then introduce the c-Eval plot, which not only displays a strong connection between c-Eval and explainers' quality, but also helps automatically determine explainer's parameters.

Identifier

85125326364 (Scopus)

ISBN

[9781665439022]

Publication Title

Proceedings 2021 IEEE International Conference on Big Data Big Data 2021

External Full Text Location

https://doi.org/10.1109/BigData52589.2021.9671895

First Page

927

Last Page

937

Grant

1939725

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