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Minimum Class Variance SVM+ for data classification

Minimum Class Variance SVM+ for data classification In this paper, a new Support Vector Machine Plus (SVM+) type model called Minimum Class Variance SVM+ (MCVSVM+) is presented. Similar to SVM+, the proposed model utilizes the group information in the training data. We show that MCVSVM+ has both the advantages of SVM+ and Minimum Class Variance Support Vector Machine (MCVSVM). That is, MCVSVM+ not only considers class distribution characteristics in its optimization problem but also utilizes the additional information (i.e. group information) hidden in the data, in contrast to SVM+ that takes into consideration only the samples that are in the class boundaries. The experimental results demonstrate the validity and advantage of the new model compared with the standard SVM, SVM+ and MCVSVM. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Data Analysis and Classification Springer Journals

Minimum Class Variance SVM+ for data classification

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References (25)

Publisher
Springer Journals
Copyright
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Subject
Statistics; Statistical Theory and Methods; Statistics for Business/Economics/Mathematical Finance/Insurance; Statistics for Life Sciences, Medicine, Health Sciences; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law; Data Mining and Knowledge Discovery
ISSN
1862-5347
eISSN
1862-5355
DOI
10.1007/s11634-015-0212-z
Publisher site
See Article on Publisher Site

Abstract

In this paper, a new Support Vector Machine Plus (SVM+) type model called Minimum Class Variance SVM+ (MCVSVM+) is presented. Similar to SVM+, the proposed model utilizes the group information in the training data. We show that MCVSVM+ has both the advantages of SVM+ and Minimum Class Variance Support Vector Machine (MCVSVM). That is, MCVSVM+ not only considers class distribution characteristics in its optimization problem but also utilizes the additional information (i.e. group information) hidden in the data, in contrast to SVM+ that takes into consideration only the samples that are in the class boundaries. The experimental results demonstrate the validity and advantage of the new model compared with the standard SVM, SVM+ and MCVSVM.

Journal

Advances in Data Analysis and ClassificationSpringer Journals

Published: Jun 10, 2015

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