• DocumentCode
    477147
  • Title

    A novel classifier for influenza a viruses based on SVM and logistic regression

  • Author

    Liu, Hsiang-chuan ; Liu, Shin-Wu ; Chang, Pei-chun ; Huang, Wen-chun ; Liao, Chien-hsiung

  • Author_Institution
    Dept. of Bioinf., Asia Univ., Taipei
  • Volume
    1
  • fYear
    2008
  • fDate
    30-31 Aug. 2008
  • Firstpage
    287
  • Lastpage
    291
  • 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.
  • Keywords
    diseases; genetics; medical computing; microorganisms; molecular biophysics; proteins; regression analysis; support vector machines; 5-fold cross-validation accuracy; Hurst exponent; SVM; fractal property; hemagglutinin protein; influenza; logistic regression; pandemic flu; physicochemical property; protein sequence; virus genome; Bioinformatics; Classification algorithms; Fractals; Genomics; Influenza; Logistics; Protein sequence; Support vector machine classification; Support vector machines; Viruses (medical); Hurst exponent; Influenza A viruses; Logistic regression; SVM; SVM-Logistic regression;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Wavelet Analysis and Pattern Recognition, 2008. ICWAPR '08. International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-2238-8
  • Electronic_ISBN
    978-1-4244-2239-5
  • Type

    conf

  • DOI
    10.1109/ICWAPR.2008.4635791
  • Filename
    4635791