Feature Selection Based on the Shapley Value

Cohen, Shay B.; Ruppin, Eytan; Dror, Gideon

Feature Selection Based on the Shapley Value

Shay B. Cohen, Eytan Ruppin, Gideon Dror

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Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 665-669.

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.

Shay B. Cohen, Eytan Ruppin, and Gideon Dror. 2005. Feature Selection Based on the Shapley Value. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05. IJCAI, 665–669. https://ijcai.org/proceedings/2005/76

@inproceedings{ijcai2005p76,
  title     = {Feature Selection Based on the Shapley Value},
  author    = {Cohen, Shay B. and Ruppin, Eytan and Dror, Gideon},
  booktitle = {Proceedings of the Nineteenth International Joint Conference on
               Artificial Intelligence, {IJCAI-05}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  pages     = {665--669},
  year      = {2005},
  url       = {https://ijcai.org/proceedings/2005/76},
}

TY  - CPAPER
TI  - Feature Selection Based on the Shapley Value
AU  - Cohen, Shay B.
AU  - Ruppin, Eytan
AU  - Dror, Gideon
T2  - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2005
SP  - 665
EP  - 669
UR  - https://ijcai.org/proceedings/2005/76
ER  -