Density-based penalty parameter optimization on C-SVM

Yun Liu, Jie Lian, Michael R. Bartolacci, Qing An Zeng

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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.

Original languageEnglish (US)
Article number851814
JournalScientific World Journal
StatePublished - 2014

All Science Journal Classification (ASJC) codes

  • General Biochemistry, Genetics and Molecular Biology
  • General Environmental Science


Dive into the research topics of 'Density-based penalty parameter optimization on C-SVM'. Together they form a unique fingerprint.

Cite this