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A new learning schema based on support vector for multi-classification

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Abstract

A novel learning schema SVCMR based on support vector is proposed in this paper to address M-class classification issue. It creates a tree-shaped decision frame where M/2 nodes are constructed with the three-separation model as the basic classifier. A class selection rule is defined to ensure basic classifiers be trained in turn on pair of classes with maximum feature distance. Class contours are extracted as data representatives to reduce training set size. Another point is that parameters involved in SVCMR are learned from data neighborhood, which brings adaptation to various datasets and avoids pricy cost spent on searching parameter spaces. Experiments on real datasets demonstrate the performance of SVCMR can be competitive to those state-of-the-art classifiers but with the higher effectiveness than them.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No. 60433020; 985 Project: Technological Creation Support of Computation and Software Science; and the Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China.

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Authors and Affiliations

  1. College of Computer Science, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, 130012, China

    Ling Ping & Zhou Chun-Guang

  2. School of Computer Science, Xuzhou Normal University, Xuzhou, 221116, China

    Ling Ping

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  1. Ling Ping
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  2. Zhou Chun-Guang
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Correspondence to Ling Ping.

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Ping, L., Chun-Guang, Z. A new learning schema based on support vector for multi-classification. Neural Comput & Applic 17, 119–127 (2008). https://doi.org/10.1007/s00521-007-0097-7

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  • DOI: https://doi.org/10.1007/s00521-007-0097-7

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