Rule extraction from support vector machines: A sequential covering approach

Barakat, Nahla H. and Bradley, Andrew P. (2007) Rule extraction from support vector machines: A sequential covering approach. IEEE Transactions on Knowledge and Data Engineering, 19 6: 729-741. doi:10.1109/TKDE.2007.1023

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Barakat, Nahla H.
Bradley, Andrew P.
Title Rule extraction from support vector machines: A sequential covering approach
Journal name IEEE Transactions on Knowledge and Data Engineering   Check publisher's open access policy
ISSN 1041-4347
Publication date 2007
Sub-type Article (original research)
DOI 10.1109/TKDE.2007.1023
Volume 19
Issue 6
Start page 729
End page 741
Total pages 13
Editor F. B. Bastani
S. McConnell
Place of publication USA
Publisher IEEE Computer Society
Collection year 2008
Language eng
Subject 280207 Pattern Recognition
730111 Hearing, vision, speech and their disorders
C1
Abstract In this paper, we propose a novel algorithm for rule extraction from support vector machines ( SVMs), termed SQRex-SVM. The proposed method extracts rules directly from the support vectors ( SVs) of a trained SVM using a modified sequential covering algorithm. Rules are generated based on an ordered search of the most discriminative features, as measured by interclass separation. Rule performance is then evaluated using measured rates of true and false positives and the area under the receiver operating characteristic ( ROC) curve ( AUC). Results are presented on a number of commonly used data sets that show the rules produced by SQRex-SVM exhibit both improved generalization performance and smaller more comprehensible rule sets compared to both other SVM rule extraction techniques and direct rule learning techniques.
Keyword Computer Science, Artificial Intelligence
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 25 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 34 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 22 Apr 2008, 12:06:19 EST by Donna Clark on behalf of School of Information Technol and Elec Engineering