Please use this identifier to cite or link to this item: https://doi.org/10.1142/S0129183103004759
Title: Support vector machine classification of physical and biological datasets
Authors: Cai, C.-Z. 
Wang, W.-L.
Chen, Y.-Z. 
Keywords: Algorithm
Classifier
DNA-binding proteins
Neural network
Sonar signal
Support vector machine
Issue Date: Jun-2003
Source: Cai, C.-Z.,Wang, W.-L.,Chen, Y.-Z. (2003-06). Support vector machine classification of physical and biological datasets. International Journal of Modern Physics C 14 (5) : 575-585. ScholarBank@NUS Repository. https://doi.org/10.1142/S0129183103004759
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.
Source Title: International Journal of Modern Physics C
URI: http://scholarbank.nus.edu.sg/handle/10635/53199
ISSN: 01291831
DOI: 10.1142/S0129183103004759
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