SUPPORT VECTOR MACHINE CLASSIFICATION OF PHYSICAL AND BIOLOGICAL DATASETS

Cong-Zhong Cai (), Wan-Lu Wang and Yu-Zong Chen ()
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Cong-Zhong Cai: Department of Applied Physics, Chongqing University, Chongqing 400044, P. R. China;
Wan-Lu Wang: Department of Applied Physics, Chongqing University, Chongqing 400044, P. R. China
Yu-Zong Chen: Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543, Singapore

International Journal of Modern Physics C (IJMPC), 2003, vol. 14, issue 05, 575-585

Abstract: The support vector machine (SVM) is used in the classification of sonar signals and DNA-binding proteins. Our study on the classification of sonar signals shows that SVM produces a result better than that obtained from other classification methods, which is consistent from the findings of other studies. The testing accuracy of classification is 95.19% as compared with that of 90.4% from multilayered neural network and that of 82.7% from nearest neighbor classifier. From our results on the classification of DNA-binding proteins, one finds that SVM gives a testing accuracy of 82.32%, which is slightly better than that obtained from an earlier study of SVM classification of protein–protein interactions. Hence, our study indicates the usefulness of SVM in the identification of DNA-binding proteins. Further improvements in SVM algorithm and parameters are suggested.

Keywords: Support vector machine; classifier; algorithm; neural network; sonar signal; DNA-binding proteins (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1142/S0129183103004759

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