Improvements in RB
Improvements in RBF Kernel using Evolutionary Algorithm for Support Vector Machine Classifier
1N.T.Renukadevi and 2P.Thangaraj
1Assistant Professor, Dept.of CT-UG, Kongu Engineering College, Perundurai, Tamilnadu
2Professor and Head, Dept. of CSE, Bannariamman Institute of Technology, Sathy, Tamilnadu
Abstract: The automatic medical image classification is useful in building a content-based medical image retrieval system. In this paper, a classification system for CT Medical Images is presented. Coiflet wavelets are used to extract feature from the CT images. The extracted features are then classified using Support Vector Machine (SVM) with Radial Basis Function (RBF). The accuracy of the SVM depends on the parameters C and gamma of the RBF kernel. The parameter selection is treated as an optimization problem wherein a search technique is used to the optimal parameters to maximize the SVM performance. In this work, Particle Swarm Optimization (PSO) is implemented to select the values of two SVM parameters for classification problems. The PSO is further modified using Genetic Algorithm to achieve optimal parameter values for the RBF kernel.
[N.T.Renukadevi and P.Thangaraj. Improvements in RBF Kernel using Evolutionary Algorithm for Support Vector Machine Classifier. Life Sci J 2013;10(7s): 454-459] (ISSN:1097-8135). http://www.lifesciencesite.com. 70
Keywords: Content Based Image Retrieval (CBIR), Computed Tomography (CT), Coiflet wavelets, Support Vector Machine (SVM), Radial Basis Function (RBF), Particle Swarm Optimization (PSO), Genetic Algorithm