title: Density-based penalty parameter optimization on C-SVM.
abstract: 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.
authors.affiliation: Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Lian
authors.affiliation: Information Sciences and Technology, Penn State University-Berks, Reading, PA 19610, USA.
authors.forename: Michael R
authors.initials: MR
authors.lastname: Bartolacci
authors.affiliation: Department of Electronics, Computer and Information Technology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA.
authors.forename: Qing-An
authors.initials: QA
authors.lastname: Zeng
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical; D060388:Support Vector Machine