Abstract
This paper presents a new algorithm to deal with nominal attributes in Support Vector Classification by modifying the most popular approach. For a nominal attribute with M states, we translate it into M points in M – 1 dimensional space with flexible and adjustable position. Their final position is decided by minimizing the Leave-one-out error. This strategy overcomes the shortcoming in the most popular approach which assume that any two different attribute values have the same degree of dissimilarities. Preliminary experiments also show the superiority of our new algorithm.
This work is supported by the National Natural Science Foundation of China (No.10371131).
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© 2005 Springer-Verlag Berlin Heidelberg
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Tian, Y., Deng, N. (2005). Support Vector Classification with Nominal Attributes. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_86
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DOI: https://doi.org/10.1007/11596448_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
Online ISBN: 978-3-540-31599-5
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