Affiliations: [a] CICAEET, Jiangsu Engineering Center of Network Monitoring,
Nanjing University of Information Science and Technology,
Nanjing 210044, Jiangsu, China | [b] School of Computer and Software, Nanjing University of Information Science and Technology, Jiangsu, Nanjing 210044, Jiangsu, China | [c] School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
Correspondence:
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Corresponding author: Tinghuai Ma, CICAEET, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China. E-mail: thma@nuist.edu.cn.
Abstract: As a combinatorial optimization problem, feature selection has been widely used in machine learning and data mining. In this paper, a feature selection method using forest optimization algorithm based on contribution degree is proposed. The proposed method uses a contribution degree strategy which is embedded in forest optimization algorithm. The goal of the contribution degree is to guide the search process of the forest optimization algorithm to select features according to high class correlation and low redundancy between features. The proposed algorithm is verified on some data sets from the UCI repository and the experiments show that the proposed method improves the classification accuracy compared with some other methods.
