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
Link To Document :
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