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

Research paper by Shifei Ding, Huajuan Huang, Junzhao Yu, Han Zhao

Indexed on: 18 Jan '13Published on: 18 Jan '13Published in: Artificial Intelligence Review



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.