NDLI: Cost-Free Learning for Support Vector Machines with a Reject Option

Content Provider	IEEE Xplore Digital Library
Author	Guibiao Xu Bao-Gang Hu
Copyright Year	2013
Description	Author affiliation: Inst. of Autom., NLPR, Beijing, China (Guibiao Xu; Bao-Gang Hu)
Abstract	In this work, we investigate into the abstaining classification of binary support vector machines (SVMs) based on mutual information (MI). We obtain the reject rule by maximizing the MI between the true labels and the predicted labels, which is a post-processing method. The gradient and Hessian matrix of MI are derived explicitly so that Newton method is used for the optimization which converges very fast. Different from the existing reject rules of SVM, the present MI-based reject rule does not require any explicit cost information and is under the framework of cost-free learning. As a matter of fact, the cost information embedded in MI can also be derived from the method, which provides an objective or initial reference to users if they want to apply cost-sensitive learning. Numerical results confirm the benefits of the proposed MI-based reject rule in comparison with other reject rules of SVM.
Sponsorship	Toshiba
Starting Page	817
Ending Page	824
File Size	385150
Page Count	8
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.45
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training support vector machines abstaining classification Bayes methods Computational efficiency mutual information Newton method Optimization Mutual information
Content Type	Text
Resource Type	Article

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