Abstract
This paper is concerned with the fuzzy support vector classification, in which both of the type of the output training point and the value of the final fuzzy classification function are triangle fuzzy number. First, the fuzzy classification problem is formulated as a fuzzy chance constrained programming. Then, we transform this programming into its equivalence quadratic programming. Final, a fuzzy support vector classification algorithm is proposed to deal with the problem. An example is presented to illustrate rationality of the algorithm.
Supported by National Natural Science Pivot Foundation of China (No.10631070) and Natural Science Foundation of Zhejiang Province (No.Y606082).
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© 2009 Springer-Verlag Berlin Heidelberg
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Yang, Z., Yang, X., Zhang, B. (2009). Fuzzy Support Vector Classification Based on Fuzzy Optimization. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_15
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DOI: https://doi.org/10.1007/978-3-642-04020-7_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04019-1
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