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

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Accession number:20084911756940

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

Authors:Liu, Hsiang-Chuan (1); Liu, Shin-Wu (2); Chang, Pei-Chun (1); Huang, Wen-Chun (3); Liao, Chien-Hsiung (1)

Author affiliation:(1) Department of Bioinformatics, Asia University, Taiwan; (2) National Institute of Allergy and Infectious Diseases, National Institutes of Health, United States; (3) Graduate Institute of Educational Measurement and Statistics, Taichung University, Taiwan Corresponding author:Liu, H.-C.

([email protected])

Source title:Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR

Abbreviated source title:Proc. Int. Conf. Wavelet Analysis and Pattern Recognition, ICWAPR

Volume:1

Monograph title:Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR

Issue date:2008

Publication year:2008 Pages:287-291

Article number:4635791 Language:English

ISBN-13:9781424422395

Document type:Conference article (CA)

Conference name:2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR

Conference date:August 30, 2008 - August 31, 2008 Conference location:Hong Kong, China

Conference code:74125

Publisher:Inst. of Elec. and Elec. Eng. Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States 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

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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.

Number of references:14

Main heading:Regression analysis

Controlled terms:Classifiers - Feature extraction - Learning systems - Logistics - Microorganisms - Pattern recognition - Support vector machines - Viruses - Wavelet analysis - Wavelet transforms

Uncontrolled terms:Hurst exponent - Influenza A viruses - Logistic regression - SVM - SVM-Logistic regression

Classification code:922.2 Mathematical Statistics - 731.5 Robotics - 741.1 Light/Optics - 751.1 Acoustic Waves - 801.2 Biochemistry - 802.1 Chemical Plants and Equipment - 912 Industrial Engineering and Management - 921 Mathematics - 921.3 Mathematical

Transformations - 723.5 Computer Applications - 404.1 Military Engineering - 461.4 Ergonomics and Human Factors Engineering - 461.9 Biology - 723.4 Artificial Intelligence - 703.2.1 Electric Filter Analysis - 723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 716

Telecommunication; Radar, Radio and Television DOI:10.1109/ICWAPR.2008.4635791

Database:Compendex