Density-Based Penalty Parameter Optimization on C-SVM
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/851814 |
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| author | Yun Liu Jie Lian Michael R. Bartolacci Qing-An Zeng |
| author_facet | Yun Liu Jie Lian Michael R. Bartolacci Qing-An Zeng |
| author_sort | Yun Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ec0a2d18f4cc406fb28add265dd6d978 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-ec0a2d18f4cc406fb28add265dd6d9782025-02-03T00:59:22ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/851814851814Density-Based Penalty Parameter Optimization on C-SVMYun Liu0Jie Lian1Michael R. Bartolacci2Qing-An Zeng3Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, ChinaKey Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, ChinaInformation Sciences and Technology, Penn State University-Berks, Reading, PA 19610, USADepartment of Electronics, Computer and Information Technology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USAThe 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.http://dx.doi.org/10.1155/2014/851814 |
| spellingShingle | Yun Liu Jie Lian Michael R. Bartolacci Qing-An Zeng Density-Based Penalty Parameter Optimization on C-SVM The Scientific World Journal |
| title | Density-Based Penalty Parameter Optimization on C-SVM |
| title_full | Density-Based Penalty Parameter Optimization on C-SVM |
| title_fullStr | Density-Based Penalty Parameter Optimization on C-SVM |
| title_full_unstemmed | Density-Based Penalty Parameter Optimization on C-SVM |
| title_short | Density-Based Penalty Parameter Optimization on C-SVM |
| title_sort | density based penalty parameter optimization on c svm |
| url | http://dx.doi.org/10.1155/2014/851814 |
| work_keys_str_mv | AT yunliu densitybasedpenaltyparameteroptimizationoncsvm AT jielian densitybasedpenaltyparameteroptimizationoncsvm AT michaelrbartolacci densitybasedpenaltyparameteroptimizationoncsvm AT qinganzeng densitybasedpenaltyparameteroptimizationoncsvm |