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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Honghai, F., Baoyan, L., Cheng, Y., Ping, L., Bingru, Y., Yumei, C. (2005). Using Rough Set to Reduce SVM Classifier Complexity and Its Use in SARS Data Set. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_82
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DOI: https://doi.org/10.1007/11553939_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28896-1
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