PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods

Yukai Yao,Hongmei Cui,Longjie Li,Yang Liu,Xiaoyun Chen,Long Zhang

doi:10.1155/2015/320186

Abstract

We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.

Highlights

The swift development of machine learning technologies gives us a good chance to process and analyse data in a brandnew perspective
To study the necessity of our System Normalization process, lots of experiments are done on our datasets, and the results show that System Normalization exerts a great influence on the classification result of Support Vector Machine (SVM); that is, it can effectively advance the classification accuracy of SVM
An optimized Support Vector Machine classifier, named PMSVM, is proposed, in which System Normalization, Principal Components Analysis (PCA), and Multilevel Grid Search (MGS) methods are used to try to average up the performances of SVM

Summary

Introduction

The swift development of machine learning technologies gives us a good chance to process and analyse data in a brandnew perspective. Known as knowledge discovery, is one of the most important branches of computer science, which aims to find useful patterns from data and is quite different from those traditional statistical methods. As a comparatively new machine learning algorithm, Support Vector Machine (SVM) has attracted much attentions recently and has been successfully used in various application vocations [1,2,3,4,5,6]. We focus on constructing an optimized SVM model, so as to use it on heart disease data classification, aiming to improve the classification efficiency and accuracy of SVM. Many literatures have involved contents of using Support Vector Machine to deal with data. Xie and Wang integrated a hybrid feature selection method with SVM for erythematosquamous disease diagnosis, which reached the accuracy of 98.61% [8]

Objectives

Results

Conclusion