Utility-based Weighted Multicategory Robust Support Vector Machines.

TitleUtility-based Weighted Multicategory Robust Support Vector Machines.
Publication TypeJournal Article
Year of Publication2010
AuthorsLiu, Yufeng, Yichao Wu, and Qinying He
JournalStat Interface
Volume3
Issue4
Pagination465-476
Date Published2010 Oct 01
ISSN1938-7989
Abstract

The Support Vector Machines (SVM) has been an important classification technique in both machine learning and statistics communities. The robust SVM is an improved version of the SVM so that the resulting classifier can be less sensitive to outliers. In many practical problems, it may be advantageous to use different weights for different types of misclassification. However, the existing RSVM treats different kinds of misclassification equally. In this paper, we propose the weighted RSVM, as an extension of the standard SVM. We show that surprisingly, the cost-based weights do not work well for weighted extensions of the RSVM. To solve this problem, we propose a novel utility-based weights for the weighted RSVM. Both theoretical and numerical studies are presented to investigate the performance of the proposed weighted multicategory RSVM.

DOI10.4310/sii.2010.v3.n4.a5
Short TitleUtility-based weighted multicategory robust support vector machines.
Alternate JournalStat Interface
Original PublicationUtility-based weighted multicategory robust support vector machines.
PubMed ID23894688
PubMed Central IDPMC3722909
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 CA149569 / CA / NCI NIH HHS / United States
R01 CA149569-01 / CA / NCI NIH HHS / United States