Feature selection based on the shapley value

Shay Cohen, Eytan Ruppin, Gideon Dror

Research output: Contribution to journalConference articlepeer-review


We present and study the Contribution-Selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the Multi-perturbation 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 elimination variant of CSA leads to the most accurate classification results on an array of datasets.

Original languageEnglish
Pages (from-to)665-670
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2005
Event19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom
Duration: 30 Jul 20055 Aug 2005


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