Feature selection based on the Shapley value

Authors:
Shay Cohen;Eytan Ruppin;Gideon Dror
Affiliations:
School of Computer Sciences, Tel-Aviv University, Tel-Aviv, Israel;School of Computer Sciences, Tel-Aviv University, Tel-Aviv, Israel;Department of Computer Science, Academic College of Tel-Aviv Yaffo, Tel-Aviv, Israel
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

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Abstract

We present and study the Contribution-Selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the Multiperturbation Shapley Analysis, a framework which relies on game theory to estimate usefulness. The algorithm iteratively estimates the usefulness of features and selects them accordingly, using either forward selection or backward elimination. Empirical comparison with several other existing feature selection methods shows that the backward eliminati-nation variant of CSA leads to the most accurate classification results on an array of datasets.