Paper Info
Title
A Fuzzy Classifier System Using the Pittsburgh Approach
Abstract
This paper describes a fuzzy classifier system using the Pittsburgh model. In this model genetic operations and fitness assignment apply to complete rule-sets, rather than to individual rules, thus overcoming the problem of conflicting individual and collective interests of classifiers. The fuzzy classifier system presented here dynamically adjusts both membership functions and fuzzy relations. A modified crossover operator for particular use in Pittsburgh-style fuzzy classifier systems, with variable length rule-sets, is introduced and evaluated. Experimental results of the new system, which appear encouraging, are presented and discussed.
Year
DOI
Venue
1994
10.1007/3-540-58484-6_270
PPSN
Keywords
Field
DocType
pittsburgh approach,fuzzy classifier system,membership function,genetic operator
Neuro-fuzzy,Fuzzy classification,Defuzzification,Computer science,Fuzzy set operations,Fuzzy logic,Fuzzy mathematics,Artificial intelligence,Membership function,Machine learning,Fuzzy rule
Conference
Volume
ISSN
ISBN
866
0302-9743
3-540-58484-6
Citations 
PageRank 
References 
13
0.97
10
Authors
2
Name
Order
Citations
PageRank
Brian Carse125926.31
T C Fogarty21147152.53