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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107027

    Title: An Improved Particle Swarm Optimization for Feature Selection
    Authors: Chen, L.-F.;Su, C.-T.;Chen, K.-H.
    Keywords: Feature selection;particle swarm optimization;genetic algorithms;sequential search algorithms
    Date: 2012-12-01
    Issue Date: 2016-08-15
    Publisher: I O S Press
    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.
    Relation: Intelligent Data Analysis 16(2), pp.167-182
    DOI: 10.3233/IDA-2012-0517
    Appears in Collections:[企業管理學系暨研究所] 期刊論文

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