Adversarial Infidelity Learning for Model Interpretation

Liang Jian

[0]

Bai Bing

[0]

Cao Yuren

Bai Kun

[0]

Wang Fei

[0]

KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event CA USA July, 2020, pp. 286-296, 2020.

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Keywords:

Time (by second) Per Sampledirect model-interpretationinstance wise feature selectionmodel interpretationinfidelityMore(20+)

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We propose an adversarial infidelity learning mechanism to screen relative unimportant features for mitigating the combinatorial shortcuts and the model identifiability problems

Abstract:

Model interpretation is essential in data mining and knowledge discovery. It can help understand the intrinsic model working mechanism and check if the model has undesired characteristics. A popular way of performing model interpretation is Instance-wise Feature Selection (IFS), which provides an importance score of each feature represent...More

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