文章基本信息

标题：Utility-based weighted multicategory robust support vector machines
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作者：Qinying He ; Yufeng Liu ; Yichao Wu 等
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2010
卷号：3
期号：4
页码：465-475
DOI：10.4310/SII.2010.v3.n4.a5
出版社：International Press
摘要：The Support Vector Machine (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 weighting scheme for the weighted RSVM. Both theoretical and numerical studies are presented to investigate the performance of the proposed weighted multicategory RSVM.
关键词：multicategory classification; robustness; SVM; utility; weighted learning