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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107036

    Title: A new particle swarm feature selection method for classification
    Authors: Chen, K.-H.;Chen, L.-F.;Su, C.-T.
    Keywords: Feature selection;Particle swarm optimization;Regression;Genetic algorithms;Sequential search algorithms
    Date: 2014-06-01
    Issue Date: 2016-08-15
    Publisher: Springer New York LLC
    Abstract: Searching for an optimal feature subset from 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 are extensively adopted to solve such problems efficiently. This study proposes a regression-based particle swarm optimization for feature selection problem. The proposed algorithm can increase population diversity and avoid local optimal trapping by improving the jump ability of flying particles. The data sets collected from UCI machine learning databases are used to evaluate the effectiveness of the proposed approach. Classification accuracy is used as a criterion to evaluate classifier performance. Results show that our proposed approach outperforms both genetic algorithms and sequential search algorithms.
    Relation: Journal of Intelligent Information Systems 42(3), pp.507-530
    DOI: 10.1007/s10844-013-0295-y
    Appears in Collections:[Graduate Institute & Department of Business Administration] Journal Article

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