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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107035

    Title: Applying Particle Swarm Optimization-Based Decision Tree Classifier for Cancer Classification on Gene Expression Data
    Authors: Chen, K.-H.;Wang, K.-J.;Wang, K.-M.;Adrian, A-M.
    Keywords: Cancer classification;Gene expression;Particle swarm optimization;C4.5
    Date: 2014-11-01
    Issue Date: 2016-08-15
    Publisher: Elsevier BV
    Abstract: Background

    The application of microarray data for cancer classification is important. Researchers have tried to analyze gene expression data using various computational intelligence methods.


    We propose a novel method for gene selection utilizing particle swarm optimization combined with a decision tree as the classifier to select a small number of informative genes from the thousands of genes in the data that can contribute in identifying cancers.


    Statistical analysis reveals that our proposed method outperforms other popular classifiers, i.e., support vector machine, self-organizing map, back propagation neural network, and C4.5 decision tree, by conducting experiments on 11 gene expression cancer datasets.
    Relation: Applied Soft Computing 24, pp.773-780
    DOI: 10.1016/j.asoc.2014.08.032
    Appears in Collections:[企業管理學系暨研究所] 期刊論文

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