The DOI matches its Crossref record; 28 of the 33 references it cites were found in a registry or an index.
Feature Selection via Coalitional Game Theory
Open accessAbstract
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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“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
In the citing paper
“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
Citations
Cites
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Shapley, L. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511528446.003 · Cited by 10,698
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 9,096
Advances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809
Making Large-Scale SVM Learning PracticalPreprint
Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,517
Alon, U. S., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S. E., Mack, D. H., & Levine, A. J. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.12.6745 · Cited by 4,170
Selection of relevant features and examples in machine learning
Blum, A. L., & Pat Langley, B. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00063-5 · Cited by 3,294
A Method for Evaluating the Distribution of Power in a Committee System
Shapley, L. S., & Shubik, M. (1954) · American Political Science Review · doi:10.2307/1951053 · Cited by 2,205
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Sorted by citations
Items 101–102 of 102
Bian, Y., Rong, Y., Xu, T., Wu, J., Krause, A., & Huang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02938 · Cited by 3
Understanding and Optimizing Agentic Workflows via Shapley value
Yang, Y., Huang, B., Qi, S., Feng, C., Hu, H., Zhu, Y., Hu, J., Zhao, H., He, Z., Liu, X., et al. (2025) · arXiv · doi:10.48550/arxiv.2502.00510 · Cited by 2
Metadata
- Authors
- Cohen, S., Dror, G., & Ruppin, E.
- Affiliations
- Tel Aviv University (IL)
- Academic College of Tel Aviv-Yafo (IL)
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- 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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