Support vector classification with nominal attributes

Authors:
Yingjie Tian;Naiyang Deng
Affiliations:
Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy, Beijing, China;College of Science, China Agricultural University, Beijing, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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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.