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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/58497


    Title: Hybrid Feature Selection by Combining Filters and Wrappers
    Authors: Hsu, Hui-Huang;Hsieh, Cheng-Wei;Lu, Ming-Da
    Contributors: 淡江大學資訊工程學系
    Keywords: Feature selection;Filters;Wrappers;Support vector machine;Disordered protein;Microarray
    Date: 2011-07
    Issue Date: 2011-10-01 12:00:56 (UTC+8)
    Publisher: Kidlington: Pergamon
    Abstract: Feature selection aims at finding the most relevant features of a problem domain. It is very helpful in improving computational speed and prediction accuracy. However, identification of useful features from hundreds or even thousands of related features is a nontrivial task. In this paper, we introduce a hybrid feature selection method which combines two feature selection methods – the filters and the wrappers. Candidate features are first selected from the original feature set via computationally-efficient filters. The candidate feature set is further refined by more accurate wrappers. This hybrid mechanism takes advantage of both the filters and the wrappers. The mechanism is examined by two bioinformatics problems, namely, protein disordered region prediction and gene selection in microarray cancer data. Experimental results show that equal or better prediction accuracy can be achieved with a smaller feature set. These feature subsets can be obtained in a reasonable time period.
    Relation: Expert Systems with Applications 38(7), pp.8144-8150
    DOI: 10.1016/j.eswa.2010.12.156
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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