Hybrid rule-extraction from support vector machines

Diederich, Joachim and Barakat, Nahla (2004). Hybrid rule-extraction from support vector machines. In: 2004 IEEE Conference on Cybernetics and Intelligent Systems. 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, (1271-1276). 1-3 December 2004. doi:10.1109/ICCIS.2004.1460774

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Author Diederich, Joachim
Barakat, Nahla
Title of paper Hybrid rule-extraction from support vector machines
Conference name 2004 IEEE Conference on Cybernetics and Intelligent Systems
Conference location Singapore
Conference dates 1-3 December 2004
Proceedings title 2004 IEEE Conference on Cybernetics and Intelligent Systems
Journal name 2004 Ieee Conference On Cybernetics and Intelligent Systems, Vols 1 and 2
Place of Publication Piscataway, NJ, U.S.A.
Publisher IEEE
Publication Year 2004
Sub-type Fully published paper
DOI 10.1109/ICCIS.2004.1460774
Open Access Status File (Author Post-print)
ISBN 0-7803-8643-4
Volume 2
Start page 1271
End page 1276
Total pages 6
Language eng
Abstract/Summary Rule-extraction from artificial neural networks(ANNs) as well as support vector machines (SVMs) provide explanations for the decisions made by these systems. This explanation capability is very important in applications such as medical diagnosis. Over the last decade, a multitude of algorithms for rule-extraction from ANNs have been developed. However, rule-extraction from SVMs is not widely available yet.In this paper, a hybrid approach for rule-extraction from SVMs is outlined. This approach has two basic components: (1) data reduction using a logistic regression model and (2) learning based rule-extraction. The quality of the extracted rules is then evaluated in terms of fidelity, accuracy, consistency and comprehensibility. The rules are also verified against the available knowledge from the domain problem (diabetes) to assure correctness and validity.
Subjects 280200 Artificial Intelligence and Signal and Image Processing
Keyword Data reduction
Knowledge acquisition
Regression analysis
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Created: Thu, 07 Apr 2005, 10:00:00 EST by Joachim Diederich on behalf of Centre for On-Line Health