Loading...
Rule extraction for support vector machine using input space expansion
Author(s)
Pitiranggon, Prasan
Benjathepanun, Nunthika
Banditvilai, Somsri
Boonjing, Veera
Date Issued
December 1, 2011
Type
Conference Paper
Abstract
Fuzzy Rule-Based System (FRB) in the form of human comprehensible IF-THEN rules can be extracted from Support Vector Machine (SVM) which is regarded as a black-boxed system. We first prove that SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS) are equivalent indicating that SVM's decision can actually be represented by fuzzy IFTHEN rules. We then propose a rule extraction method based on kernel function firing strength and unbounded support vector space expansion. An advantage of our method is the guarantee that the number of final fuzzy IF-THEN rules is equal or less than the number of support vectors in SVM, and it may reveal human comprehensible patterns. We compare our method against SVM using popular benchmark data sets, and the results are comparable. © 2011 Springer-Verlag Berlin Heidelberg.
Citation
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 6592 LNAI(PART 2), 100-109, 2011