Title :
Extraction of Fuzzy Rules by Using Support Vector Machines
Author :
Chen, Shuwei ; Wang, Jie ; Wang, Dongshu
Author_Institution :
Sch. of Electr. Eng., Zhengzhou Univ., Zhengzhou
Abstract :
This paper proposes an architecture to extract fuzzy rules based on support vector machines (SVMs). Firstly, support vectors are obtained from the training data set to generate fuzzy if-then rules with membership functions described in terms of kernel functions via support vector machine learning procedure. Then, a combined fuzzy rule base is created based on both the generated rules and linguistic rules of human experts. Thus, it has the inherent advantages that the rule base is optimized automatically during the SVM learning procedure, and, takes both "subjective" experts\´ prior knowledge and "objective"\´ training data into account. An example is given to show the effectiveness of the proposed method.
Keywords :
fuzzy set theory; inference mechanisms; learning (artificial intelligence); support vector machines; SVMs; fuzzy if-then rules; fuzzy rules extraction; support vector machines; Data mining; Fuzzy sets; Fuzzy systems; Humans; Kernel; Learning systems; Machine learning; Statistical learning; Support vector machines; Training data; Fuzzy rule extraction; kernel function; support vector machine;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on
Conference_Location :
Shandong
Print_ISBN :
978-0-7695-3305-6
DOI :
10.1109/FSKD.2008.453