Feature selection via coalitional game theory

Shay Cohen*, Gideon Dror, Eytan Ruppin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

97 Scopus citations


We present and study the contribution-selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the multiperturbation shapley analysis (MSA), a framework that 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. It can optimize various performance measures over unseen data such as accuracy, balanced error rate, and area under receiver-operator- characteristic curve. 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 data sets.

Original languageEnglish
Pages (from-to)1939-1961
Number of pages23
JournalNeural Computation
Issue number7
StatePublished - Jul 2007


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