%0 Journal Article
	%A Prasan Pitiranggon and  Nunthika Benjathepanun and  Somsri Banditvilai and  Veera Boonjing
	%D 2010
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 44, 2010
	%T Fuzzy Rules Generation and Extraction from Support Vector Machine Based on Kernel Function Firing Signals
	%U https://publications.waset.org/pdf/4759
	%V 44
	%X Our study proposes an alternative method in building
Fuzzy Rule-Based System (FRB) from Support Vector Machine
(SVM). The first set of fuzzy IF-THEN rules is obtained through
an equivalence of the SVM decision network and the zero-ordered
Sugeno FRB type of the Adaptive Network Fuzzy Inference System
(ANFIS). The second set of rules is generated by combining the
first set based on strength of firing signals of support vectors using
Gaussian kernel. The final set of rules is then obtained from the
second set through input scatter partitioning. A distinctive advantage
of our method is the guarantee that the number of final fuzzy IFTHEN
rules is not more than the number of support vectors in the
trained SVM. The final FRB system obtained is capable of performing
classification with results comparable to its SVM counterpart, but it
has an advantage over the black-boxed SVM in that it may reveal
human comprehensible patterns.
	%P 1209 - 1216