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The Scientific World JournalVolume 2014 2014, Article ID 851814, 9 pages

Research Article

Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China

Information Sciences and Technology, Penn State University-Berks, Reading, PA 19610, USA

Department of Electronics, Computer and Information Technology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA

Received 29 April 2014; Accepted 4 June 2014; Published 7 July 2014

Academic Editor: Han-Chieh Chao

Copyright © 2014 Yun Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The support vector machine SVM is one of the most widely used approaches for data classification and regression. SVM achieves the largest distance between the positive and negative support vectors, which neglects the remote instances away from the SVM interface. In order to avoid a position change of the SVM interface as the result of an error system outlier, C-SVM was implemented to decrease the influences of the system’s outliers. Traditional C-SVM holds a uniform parameter C for both positive and negative instances; however, according to the different number proportions and the data distribution, positive and negative instances should be set with different weights for the penalty parameter of the error terms. Therefore, in this paper, we propose density-based penalty parameter optimization of C-SVM. The experiential results indicated that our proposed algorithm has outstanding performance with respect to both precision and recall.

Autor: Yun Liu, Jie Lian, Michael R. Bartolacci, and Qing-An Zeng



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