NDLI: Margin-Maximizing Feature Elimination Methods for Linear and Nonlinear Kernel-Based Discriminant Functions

Content Provider	IEEE Xplore Digital Library
Author	Aksu, Y. Miller, D.J. Kesidis, G. Yang, Q.X.
Copyright Year	1990
Abstract	Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature ¿markers.¿ For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom-the hyperplane's intercept and its squared 2-norm-with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.
Sponsorship	IEEE Computational Intelligence Society
Page Count	17
File Size	1565289
Starting Page	701
Ending Page	717
File Format	PDF
ISSN	10459227
Volume Number	21
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Kernel Biomedical imaging Magnetic resonance imaging Bioinformatics Decision making Linear programming Alzheimer's disease Brain support vector machine (SVM) Alzheimer's classifier margin discriminant function feature elimination Gaussian kernel margin maximization medical imaging microarray magnetic resonance imaging (MRI) neurodegenerative polynomial kernel recursive feature elimination (RFE)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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