Paper Info
Title
A Framework for Evolving Fuzzy Classifier Systems Using Genetic Programming
Abstract
A fuzzy classifier system framework is proposed which employs a tree-based representation for fuzzy rule (classifier) antecedents and genetic programming for fuzzy rule discovery. Such a rule representation is employed because of the expressive power and generality it endows to individual rules. The framework proposes accuracy-based fitness for individual fuzzy classifiers and employs evolutionary competition between simultaneously matched classifiers. The evolutionary algorithm (GP) is therefore searching for compact fuzzy rule bases which are simultaneously general, accurate and co-adapted. Additional extensions to the proposed framework are suggested.
Year
Venue
Keywords
2001
FLAIRS Conference
genetic programming,evolving fuzzy classifier systems,evolutionary algorithm,expressive power
Field
DocType
ISBN
Neuro-fuzzy,Fuzzy classification,Defuzzification,Fuzzy set operations,Computer science,Fuzzy logic,Artificial intelligence,Fuzzy number,Fuzzy associative matrix,Machine learning,Fuzzy rule
Conference
1-57735-133-9
Citations 
PageRank 
References 
6
0.89
10
Authors
2
Name
Order
Citations
PageRank
Brian Carse125926.31
Anthony G. Pipe225539.08