ML Anthology

Feature Selection Based on the Shapley Value

Shay B. Cohen, Eytan Ruppin, Gideon Dror
IJCAI 2005 pp. 665-670
/ijcai/2005/cohen2005ijcai-feature/

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 elimination variant of CSA leads to the most accurate classification results on an array of datasets. 1

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Text

Cohen et al. "Feature Selection Based on the Shapley Value." International Joint Conference on Artificial Intelligence, 2005.

Markdown

[Cohen et al. "Feature Selection Based on the Shapley Value." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/cohen2005ijcai-feature/)

BibTeX

@inproceedings{cohen2005ijcai-feature,
  title     = {{Feature Selection Based on the Shapley Value}},
  author    = {Cohen, Shay B. and Ruppin, Eytan and Dror, Gideon},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2005},
  pages     = {665-670},
  url       = {https://mlanthology.org/ijcai/2005/cohen2005ijcai-feature/}
}