期刊名称：International Journal of Advanced Computer Research
印刷版ISSN：2249-7277
电子版ISSN：2277-7970
出版年度：2012
卷号：2
期号：7
页码：130-135
出版社：Association of Computer Communication Education for National Triumph (ACCENT)
摘要：Support vector machine (SVM) has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. SVM is able to calculate the maximum margin (separating hyper-plane) between data with and without the outcome of interest if they are linearly separable. To improve the generalisation performance of SVM classifier optimization technique is used. Optimization refers to the selection of a best element from some set of available alternatives. Particle swarm optimization (PSO) is a population based stochastic optimization technique where the potential solutions, called particles, fly through the problem space by following the current optimum particles. In this paper, Principal Component Analysis (PCA) is used for reducing features of breast cancer, lung cancer and heart disease data sets and an empirical comparison of kernel selection using PSO for SVM is used to achieve better performance. This paper focused on SVM trained using linear, polynomial and radial basis function (RBF) kernels and applying PSO to each kernels for each data set to get better accuracy.
关键词：Principal Component Analysis; Support Vector Machine; Linear Kernel Function Polynomial Kernel Function; Radial Basis Function; Particle swarm optimization.
