Improvements
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
