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Research on the hybrid models of granular computing and support vector machine

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Abstract

The hybrid models of granular computing and support vector machine are a kind of new machine learning algorithms based on granular computing and statistical learning theory. These hybrid models can effectively use the advantage of each algorithm, so that their performance are better than a single method. In view of their excellent learning performance, the hybrid models of granular computing and support vector machine have become one of the focus at home and abroad. In this paper, the research on the hybrid models are reviewed, which include fuzzy support vector machine, rough support vector machine, quotient space support vector machine, rough fuzzy support vector machine and fuzzy rough support vector machine. Firstly, we briefly introduce the typical granular computing models and the basic theory of support vector machines. Secondly, we describe the latest progress of these hybrid models in recent years. Finally, we point out the research and development prospects of the hybrid algorithms.

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Acknowledgments

This work is supported by the National Key Basic Research Program of China (No. 2013CB329502), the National Natural Science Foundation of China (No.41074003), and the Opening Foundation of the Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (IIP2010-1).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Shifei Ding, Huajuan Huang, Junzhao Yu & Han Zhao

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Science, Beijing, 100190, China

    Shifei Ding

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  1. Shifei Ding
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  2. Huajuan Huang
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  3. Junzhao Yu
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  4. Han Zhao
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Correspondence to Shifei Ding.

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Ding, S., Huang, H., Yu, J. et al. Research on the hybrid models of granular computing and support vector machine. Artif Intell Rev 43, 565–577 (2015). https://doi.org/10.1007/s10462-013-9393-z

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