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Rule extraction from support vector machines: a hybrid approach for solving classification and regression problems

Rule extraction from support vector machines: a hybrid approach for solving classification and... In this paper, a novel hybrid approach to extract rules from support vector machine and support vector regression (SVM/SVR) is presented. The hybrid has three phases: Extensive experiments are conducted on three benchmark classification problems, four bank bankruptcy prediction problems and five benchmark regression problems. We conclude that the rules obtained after feature selection perform comparably to those extracted from all features. Further, comprehensibility is also improved after feature selection. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Information and Decision Sciences Inderscience Publishers

Rule extraction from support vector machines: a hybrid approach for solving classification and regression problems

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1756-7017
eISSN
1756-7025
DOI
10.1504/IJIDS.2011.041587
Publisher site
See Article on Publisher Site

Abstract

In this paper, a novel hybrid approach to extract rules from support vector machine and support vector regression (SVM/SVR) is presented. The hybrid has three phases: Extensive experiments are conducted on three benchmark classification problems, four bank bankruptcy prediction problems and five benchmark regression problems. We conclude that the rules obtained after feature selection perform comparably to those extracted from all features. Further, comprehensibility is also improved after feature selection.

Journal

International Journal of Information and Decision SciencesInderscience Publishers

Published: Jan 1, 2011

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