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

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

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 classificati...

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Main Authors: Yukai Yao, Hongmei Cui, Yang Liu, Longjie Li, Long Zhang, Xiaoyun Chen
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2015/320186
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spelling doaj-04e40d6a2e1642089d659ad2308baef22020-11-24T21:00:28ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/320186320186PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search MethodsYukai Yao0Hongmei Cui1Yang Liu2Longjie Li3Long Zhang4Xiaoyun Chen5School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaGansu Provincial Maternity and Child-Care Hospital, Lanzhou 730050, ChinaChina Mobile Communications Group, Henan Co., Ltd., Zhengzhou 450000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaWe 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.http://dx.doi.org/10.1155/2015/320186
collection DOAJ
language English
format Article
sources DOAJ
author Yukai Yao
Hongmei Cui
Yang Liu
Longjie Li
Long Zhang
Xiaoyun Chen
spellingShingle Yukai Yao
Hongmei Cui
Yang Liu
Longjie Li
Long Zhang
Xiaoyun Chen
PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods
Mathematical Problems in Engineering
author_facet Yukai Yao
Hongmei Cui
Yang Liu
Longjie Li
Long Zhang
Xiaoyun Chen
author_sort Yukai Yao
title PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods
title_short PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods
title_full PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods
title_fullStr PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods
title_full_unstemmed PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods
title_sort pmsvm: an optimized support vector machine classification algorithm based on pca and multilevel grid search methods
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2015-01-01
description 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.
url http://dx.doi.org/10.1155/2015/320186
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AT yangliu pmsvmanoptimizedsupportvectormachineclassificationalgorithmbasedonpcaandmultilevelgridsearchmethods
AT longjieli pmsvmanoptimizedsupportvectormachineclassificationalgorithmbasedonpcaandmultilevelgridsearchmethods
AT longzhang pmsvmanoptimizedsupportvectormachineclassificationalgorithmbasedonpcaandmultilevelgridsearchmethods
AT xiaoyunchen pmsvmanoptimizedsupportvectormachineclassificationalgorithmbasedonpcaandmultilevelgridsearchmethods
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