title: Utility-based Weighted Multicategory Robust Support Vector Machines.
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.
authors.affiliation: Department of Statistics and Operations Research, Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, NC 27599 ( yfliu@email.unc.edu ).
authors.forename: Yufeng
authors.initials: Y
authors.lastname: Liu
authors.forename: Yichao
authors.initials: Y
authors.lastname: Wu
authors.forename: Qinying
authors.initials: Q
authors.lastname: He
keywords: Multicategory Classification; Robustness; SVM; Utility; Weighted Learning
references.citation: Biometrics. 2009 Mar;65(1):159-68
references.pmid: 18363773
references.citation: J Am Stat Assoc. 2010 Mar 1;105(489):424-436
references.pmid: 21113386
references.citation: J Am Stat Assoc. 2010 Mar 1;105(489):401-414
references.pmid: 21152360