Affiliations: [a] Department of Business Administration, Fu-Jen Catholic University, New Taipei City, Taiwan | [b] Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan
Correspondence:
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Corresponding author: Li-Fei Chen, Assistant Professor, Department of Business Administration, Fu-Jen Catholic University, No. 510, Zhongzheng Rd., Xinzhung Dist., New Taipei City 24205, Taiwan. Tel.: +886 2 2905 2966; Fax: +886 2 2905 2753; E-mail: [email protected]
Abstract: Searching for an optimal feature subset in a high-dimensional feature space is an NP-complete problem; hence, traditional optimization algorithms are inefficient when solving large-scale feature selection problems. Therefore, meta-heuristic algorithms have been extensively adopted to solve the feature selection problem efficiently. This study proposes an improved particle swarm optimization (IPSO) algorithm using the opposite sign test (OST). The test increases population diversity in the PSO mechanism, and avoids local optimal trapping by improving the jump ability of flying particles. Data sets collected from UCI machine learning databases are used to evaluate the effectiveness of the proposed approach. Classification accuracy is employed as a criterion to evaluate classifier performance. Results show that the proposed approach outperforms both genetic algorithms and sequential search algorithms.
