Using rough set to reduce SVM classifier complexity and its use in SARS data set

Authors:
Feng Honghai;Liu Baoyan;Yin Cheng;Li Ping;Yang Bingru;Chen Yumei
Affiliations:
Urban & Rural Construction School, Hebei Agricultural University, Baoding, China;China Academy of Traditional Chinese Medicine, Beijing, China;Modern Educational Center, Hebei Agricultural University, Baoding, China;China Academy of Traditional Chinese Medicine, Beijing, China;Information Engineering School, University of Science and Technology Beijing, Beijing, China;Tian'e Chemical Fiber Company of Hebei Baoding, Baoding, China
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2005

Citing 3
Cited 0

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ACM Transactions on Intelligent Systems and Technology (TIST)

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Abstract

For SVM classifier, Pre-selecting data is necessary to achieve satisfactory classification rate and reduction of complexity. According to Rough Set Theory, the examples in boundary region of a set belong to two or more classes, lying in the boundary of the classes, and according to SVM, support vectors lie in the boundary too. So we use Rough Set Theory to select the examples of boundary region of a set as the SVM classifier set, the complexity of SVM classifier would be reduced and the accuracy maintained. Experiment results of SARS data indicate that our schema is available in both the training and prediction stages.