Cited by 144Evolutionary Algorithms and ApplicationsMachine Learning and Data ClassificationText and Document Classification Technologies

Feature Selection via Coalitional Game Theory

Open access
Cohen, Shay, Dror, Gideon, Ruppin, Eytan·2007·Neural Computation

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 (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.

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Registry

Crossref · 10.1162/neco.2007.19.7.1939

Cites

28 of 28 references verified0 citation errors · 0 not checkable

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  • The Shapley value provides a solution concept for cooperative games that fairly distributes the rewards among players according to their marginal contributions.

    “The Shapley value, introduced by (Shapley 1952; Hart 1989; Cohen, Dror, and Ruppin 2007; Lundberg and Lee 2017; Ghorbani and Zou 2019), provides a solution concept for cooperative games that fairly distributes the rewards among players according to their marginal contributions.”

    From Understanding and Optimizing Agentic Workflows via Shapley value (2025) · cites this paper as Cohen, Dror, and Ruppin 2007

  • The Shapley value is a mathematically rigorous method to quantify both individual component contributions and their interaction effects across all possible component configurations.

    “Specifically, we use the Shapley value (Shapley 1952; Hart 1989; Cohen, Dror, and Ruppin 2007; Lundberg and Lee 2017; Ghorbani and Zou 2019), a mathematically rigorous method to quantify both individual component contributions and their interaction effects across all possible component configurations.”

    From Understanding and Optimizing Agentic Workflows via Shapley value (2025) · cites this paper as Cohen, Dror, and Ruppin 2007

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Authors
Cohen, S., Dror, G., & Ruppin, E.
Affiliations
  • Tel Aviv University (IL)
  • Academic College of Tel Aviv-Yafo (IL)
Venue
Neural Computation
Published
2007-07
Volume
19
Issue
7
Pages
1939-1961
ISSN
  • 0899-7667 (ISSN-L)
  • 1530-888X
OpenAlex
W2127538534
Cited by
144
References
33
Citations by year
2026: 9 · 2025: 16 · 2024: 13 · 2023: 9 · 2022: 13 · 2021: 17 · 2020: 13 · 2019: 7 · 2018: 4 · 2016: 4 · 2015: 7 · 2014: 8 · 2013: 4 · 2012: 2
FWCI
3.95
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • Neural Computation (publishedVersion)
  • PubMed (publishedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
Keywords
feature selection, cooperative game theory, forward selection, backward elimination, classification accuracy
MeSH terms
Algorithms, Game Theory, Humans, Neural Networks, Computer

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