期刊
INTERNATIONAL CONFERENCE ON MODELLING OPTIMIZATION AND COMPUTING
卷 38, 期 -, 页码 1340-1345出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.proeng.2012.06.165
关键词
Principal Component Analysis; Support Vector Machine; Linear Kernel Function; Polynomial Kernel Function; Radial Basis Function
Classification is a vital tool for understanding the relationships of living things using which similar things can be grouped together. Classification of elements into groups makes the study relatively easy. Therefore, classification is necessary to know salient features and characteristics of living organisms as well as their inter relationship among different group of organisms, as the correct classification of a person's disease is important for proper treatment. Support vector machine (SVM) was the first proposed kernel-based method, which uses a kernel function to transfer data from input space into high dimensional feature space; it searches for a separating hyper-plane. SVM is based on simple ideas which originated in statistical learning theory; hence the aim is to solve only the problem of interest without solving a more difficult problem as an intermediate step. SVM apply a simple linear method to the data but in a high-dimensional feature space non-linearly related to the input space. Even though we can think of SVM as a linear algorithm in high dimensional space, but in practice it does not involve any computations in that high-dimensional space. As dimensionality is curse to gene expression data set, in this paper Principal Component Analysis (PCA) is used for feature reduction to breast cancer, lung cancer and cardiotography data sets, and SVM is trained by linear, polynomial and radial basis function (RBF) kernels applied on each of these data sets and the comparison among them shows that RBF is better for the three data sets. (C) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul Islam Centre for Higher Education
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据