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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8796


    Title: Fuzzy c-mean algorithm based on complete mahalanobis distances and separable criterion
    Authors: Liu, Hsiang-Chuan;Wu, Der-Bang;Yih, Jeng-Ming;Liu, Shin-Wu
    Contributors: Department of Bioinformatics
    Keywords: Covariance matrix;Diesel engines;Fuzzy clustering;Fuzzy logic;Fuzzy rules;Fuzzy systems;Knowledge based systems;Solenoids;Spheres;Cluster centers;Euclidean distances;Fcm algorithms;Fuzzy partitions;Improved algorithms;Mahalanobis distances;New algorithms;Objective functions;Prior informations;Real data sets;Structural clusters;Unsupervised algorithms
    Date: 2008
    Issue Date: 2010-04-08 20:06:11 (UTC+8)
    Publisher: Asia University
    Abstract: In search of good classifier of hosts of influenza A viruses is an important issue to prevent pandemic flu. The hemagglutinin protein in the virus genome is the major molecule that determining the range of hosts. In this paper, a novel classification algorithm of hemagglutinin proteins integrating SVM and logistic regression based on 4 kinds of Hurst exponents for each protein sequence is proposed. This method not used before is the first one integrating the physicochemical properties, fractal property, SVM and logistic regression classifier. For evaluating the performance of this new algorithm, a real data experiment by using 5-fold Cross-Validation accuracy is conducted. Experimental result shows that this new classification algorithm is useful and batter than SVM and logistic regression, respectively. ©2008 IEEE.
    Relation: Proceedings - 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008 1 :87-91
    Appears in Collections:[生物資訊與醫學工程學系 ] 會議論文

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