From explanations to feature selection: assessing SHAP values as feature selection mechanism

By Wilson Estécio Marcílio, Danilo Medeiros Eler. 2020

413 citations recorded by the source corpus.

Explainability has become one of the most discussed topics in machine learning research in recent years, and although a lot of methodologies that try to provide explanations to black-box models have been proposed to address such an issue, little discussion has been made on the pre-processing steps involving the pipeline of development of machine learning solutions, such as feature selection. In this work, we evaluate a game-theoretic approach used to explain the output of any machine learning model, SHAP, as a feature selection mechanism. In the experiments, we show that besides being able to explain the decisions of a model, it achieves better results than three commonly used feature selection algorithms.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. John T. Hancock
2. Taghi M. Khoshgoftaar
3. Huanjing Wang
4. Qianxin Liang
5. Sergey I. Nikolenko

Citations in the corpus

• Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods (2024, methodology): “Marcilio and Eler [21] employed the SHAP method as a feature selection technique and compared it against three widely used feature selection methods: Mutual Information, Recursive Feature Elimination, and ANOVA.”