Paper Info
Title
GPF-CLASS: A Genetic Fuzzy model for classification
Abstract
This work presents a Genetic Fuzzy Classification System (GFCS) called Genetic Programming Fuzzy Classification System (GPF-CLASS). This model differs from the traditional approach of GFCS, which uses the metaheuristic as a way to learn “if-then” fuzzy rules. This classical approach needs several changes and constraints on the use of genetic operators, evaluation and selection, which depends primarily on the metaheuristic used. Genetic Programming makes this implementation costly and explores few of its characteristics and potentialities. The GPF-CLASS model seeks for a greater integration with the metaheuristic: Multi-Gene Genetic Programming (MGGP), exploring its potential of terminals selection (input features) and functional form and at the same time aims to provide the user with a comprehension of the classification solution. Tests with 22 benchmarks datasets for classification have been performed and, as well as statistical analysis and comparisons with others Genetic Fuzzy Systems proposed in the literature.
Year
DOI
Venue
2013
10.1109/CEC.2013.6557971
Evolutionary Computation
Keywords
Field
DocType
fuzzy logic,fuzzy set theory,genetic algorithms,mathematical operators,pattern classification,GFCS,GPF-CLASS,MGGP,genetic fuzzy classification system,genetic fuzzy model,genetic operator,genetic programming fuzzy classification system,if-then fuzzy rule,metaheuristic,multigene genetic programming,terminals selection,classification,genetic fuzzy systems,multi-gene genetic programming
Genetic operator,Mathematical optimization,Neuro-fuzzy,Fuzzy classification,Computer science,Fuzzy set operations,Genetic programming,Artificial intelligence,Genetic representation,Genetic fuzzy systems,Genetic algorithm,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4799-0452-5
5
0.47
References 
Authors
13
5
Name
Order
Citations
PageRank
Adriano Soares Koshiyama13410.19
Tatiana Escovedo2173.72
Douglas Mota Dias3247.18
Marley B. R. Vellasco428047.47
Ricardo Tanscheit511821.53