Dynamic Granular Support Vector Machine Learning Algorithm
CHENG Feng-Wei1, WANG Wen-Jian1,2 , GUO Hu-Sheng1
1.School of Computer and Information Technology, Shanxi University, Taiyuan 030006 2.Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University, Taiyuan 030006
Abstract Granular support vector machine (GSVM) is effective when dealing with distribution uniform datasets. However, the distribution of the dataset in the real world is unpredictable, and the density is uneven. In this paper, a dynamic granular support vector machine learning algorithm(DGSVM) is proposed. According to the different distribution of the granules, some granules are divided automatically and SVM training is performed on different levels of granule space. The experimental results on benchmark datasets demonstrate that DGSVM algorithm obtains better classification performance compared with GSVM.
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2014, Vol. 27 
