Forming an efficient feature space for classification problems is a grand challenge in pattern recognition. Many optimization algorithms are adopted to do feature selection, but these algorithms do searching in multi-dimensions space and always cannot get the optimal feature subset. In this paper, a feature selection method with Particle Swarm Optimization based one-dimension searching is proposed to improve the classification performance. Experimental results show that the proposed method can do feature selection more effectively than the compared method and get much higher classification accuracy.


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    Title :

    Effective feature selection with Particle Swarm Optimization based one-dimension searching


    Contributors:
    Jun Wang, (author) / Yan Zhao, (author) / Ping Liu, (author)


    Publication date :

    2010-06-01


    Size :

    581462 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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