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Feature Selection with Neighborhood Entropy-Based Cooperative Game Theory
Joint Authors
She, Kun
Zeng, Kai
Niu, Xinzheng
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-24
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Feature selection plays an important role in machine learning and data mining.
In recent years, various feature measurements have been proposed to select significant features from high-dimensional datasets.
However, most traditional feature selection methods will ignore some features which have strong classification ability as a group but are weak as individuals.
To deal with this problem, we redefine the redundancy, interdependence, and independence of features by using neighborhood entropy.
Then the neighborhood entropy-based feature contribution is proposed under the framework of cooperative game.
The evaluative criteria of features can be formalized as the product of contribution and other classical feature measures.
Finally, the proposed method is tested on several UCI datasets.
The results show that neighborhood entropy-based cooperative game theory model (NECGT) yield better performance than classical ones.
American Psychological Association (APA)
Zeng, Kai& She, Kun& Niu, Xinzheng. 2014. Feature Selection with Neighborhood Entropy-Based Cooperative Game Theory. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1034651
Modern Language Association (MLA)
Zeng, Kai…[et al.]. Feature Selection with Neighborhood Entropy-Based Cooperative Game Theory. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1034651
American Medical Association (AMA)
Zeng, Kai& She, Kun& Niu, Xinzheng. Feature Selection with Neighborhood Entropy-Based Cooperative Game Theory. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1034651
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1034651