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Support Vector Machine Classification Of Physical And Biological Datasets

Author

Listed:
  • CONG-ZHONG CAI

    (Department of Applied Physics, Chongqing University, Chongqing 400044, P. R. China;
    Bioprocessing Technology Centre, National University of Singapore, MD11, Level 5, 10 Medical Drive, Singapore 117597, Singapore;
    Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543, Singapore)

  • 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)

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.

Suggested Citation

  • Cong-Zhong Cai & Wan-Lu Wang & Yu-Zong Chen, 2003. "Support Vector Machine Classification Of Physical And Biological Datasets," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 14(05), pages 575-585.
    • Handle: RePEc:wsi:ijmpcx:v:14:y:2003:i:05:n:s0129183103004759
    DOI: 10.1142/S0129183103004759
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    References listed on IDEAS

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