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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/95955


    Title: A Hybrid Prototype Construction and Feature Selection Method with Parameter Optimization for Support Vector Machine
    Authors: Wong, Ching-Chang;Leu, Chun-Liang
    Contributors: 淡江大學電機工程學系
    Keywords: Dynamic condensed nearest neighbor;Prototype construction;Feature selection;Genetic algorithm;Support vector machine
    Date: 2008-11
    Issue Date: 2014-02-13 11:26:25 (UTC+8)
    Abstract: In this paper, an order-independent algorithm for data reduction, called the Dynamic Condensed Nearest Neighbor (DCNN) rule, is proposed to adaptively construct prototypes in training dataset and to reduce the over-fitting affect with superfluous instances for the Support Vector Machine (SVM). Furthermore, a hybrid model based on the genetic algorithm is proposed to optimize the prototype construction, feature selection, and the SVM kernel parameters setting simultaneously. Several UCI benchmark datasets are considered to compare the proposed GA-DCNN-SVM approach with the GA-based previously published method. The experimental results show that the proposed hybrid model outperforms the existing method and improves the classification accuracy for SVM.
    Relation: Proceedings of the 2008 International Computer Symposium (ICS 2008),6頁
    Appears in Collections:[電機工程學系暨研究所] 會議論文

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