Multi-Radial Basis Function SVM Classifier: Design and Analysis

  • Wang, Zheng (School of Electrical and Electronic Engineering, The University of Suwon) ;
  • Yang, Cheng (School of Electrical and Electronic Engineering, The University of Suwon) ;
  • Oh, Sung-Kwun (School of Electrical and Electronic Engineering, The University of Suwon, Korea, and Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong, Linyi University) ;
  • Fu, Zunwei (Dept. of Computer Science, The University of Suwon, Korea, and Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong, Linyi University)
  • Received : 2018.04.30
  • Accepted : 2018.07.19
  • Published : 2018.11.01


In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.


Supported by : National Research Foundation of Korea(NRF)


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