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Received: 2003-05-07

Revision Accepted: 2003-12-14

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Journal of Zhejiang University SCIENCE A 2004 Vol.5 No.11 P.1382~1391

http://doi.org/10.1631/jzus.2004.1382


Feature selection based on mutual information and redundancy-synergy coefficient


Author(s):  YANG Sheng, GU Jun

Affiliation(s):  Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China; more

Corresponding email(s):   yangsheng@sjtu.edu.cn

Key Words:  Mutual information, Feature selection, Machine learning, Data mining


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YANG Sheng, GU Jun. Feature selection based on mutual information and redundancy-synergy coefficient[J]. Journal of Zhejiang University Science A, 2004, 5(11): 1382~1391.

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author="YANG Sheng, GU Jun",
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year="2004",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2004.1382"
}

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%DOI 10.1631/jzus.2004.1382

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T1 - Feature selection based on mutual information and redundancy-synergy coefficient
A1 - YANG Sheng
A1 - GU Jun
J0 - Journal of Zhejiang University Science A
VL - 5
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SP - 1382
EP - 1391
%@ 1869-1951
Y1 - 2004
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2004.1382


Abstract: 
mutual information is an important information measure for feature subset. In this paper, a hashing mechanism is proposed to calculate the mutual information on the feature subset. Redundancy-synergy coefficient, a novel redundancy and synergy measure of features to express the class feature, is defined by mutual information. The information maximization rule was applied to derive the heuristic feature subset selection method based on mutual information and redundancy-synergy coefficient. Our experiment results showed the good performance of the new feature selection method.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[8] Liu, H., Motoda, H., Dash, M., 1998. A Monotonic Measure for Optimal Feature Selection. Proceedings of ECML-98, p.101-106.

[9] Liu, H., Setiono, R., 1996. A Probabilistic Approach to Feature Selection – A Filter Solution. In: ICML-96. Morgan Kaufmann Publishers, p.319-327.

[10] Murphy, P.M., Pazzani, M.J., 1994. Exploring the decision forest: An empirical investigation of Occam’s razor in decision tree induction. Journal of Art. Intel., 1:257-319.

[11] Narendra, P., Fukunaga, K., 1977. A branch and bound method for feature subset selection. IEEE Trans. on Computer, 26 (9):917-922.

[12] Yaglom, A.M., Yaglom, I.M., 1983. Probability and Information. D. Reidel Publishing Company.

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