Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 38, Issue 11, Pages 14238-14248Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.04.237
Keywords
Support vector machines; Multi-class classification; One-against-all; Text categorization
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We present an improved version of one-against-all method for multiclass SVM classification based on subset sample selection, named reduced one-against-all, to achieve high performance in large multiclass problems. Reduced one-against-all drastically decreases the computing effort involved in training one-against-all classifiers, without any compromise in classification accuracy. Computational comparisons on publicly available datasets indicate that the proposed method has comparable accuracy to that of conventional one-against-all method, but with an order of magnitude faster. On the largest dataset considered, reduced one-against-all method achieved 50% reduction in computing time over one-against-all method for almost the same classification accuracy. We further investigated reduced one-against-all with linear kernel for multi-label text categorization applications. Computational results demonstrate the effectiveness of the proposed method on both the text corpuses considered. (C) 2011 Elsevier Ltd. All rights reserved.
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