Density-Based Penalty Parameter Optimization on C-SVM
Joint Authors
Lian, Jie
Bartolacci, Michael R.
Zeng, Qing-An
Liu, Yun
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-07
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Liu, Yun& Lian, Jie& Bartolacci, Michael R.& Zeng, Qing-An. 2014. Density-Based Penalty Parameter Optimization on C-SVM. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1051339
Modern Language Association (MLA)
Liu, Yun…[et al.]. Density-Based Penalty Parameter Optimization on C-SVM. The Scientific World Journal No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1051339
American Medical Association (AMA)
Liu, Yun& Lian, Jie& Bartolacci, Michael R.& Zeng, Qing-An. Density-Based Penalty Parameter Optimization on C-SVM. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1051339
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051339