Paper Info
Title
A First Study On Bagging Fuzzy Rule-Based Classification Systems With Multicriteria Genetic Selection Of The Component Classifiers
Abstract
Fuzzy rule-based classification systems (FRBCSs) are able to design interpretable classifiers but suffer from the curse of dimensionality when dealing with complex problems with a large number of features. In this contribution we explore the use of popular approaches for designing ensembles of classifiers in the machine learning field, bagging and random subspace, to design FRBCS multiclassifiers from a basic, heuristic fuzzy classification rule generation method, aiming to both improve their accuracy and to make them able to deal with high dimensional classification problems. Besides, a multicriteria genetic algorithm is proposed to select the component classifiers in the ensemble guided by the cumulative likelihood in order to look for an appropriate accuracy-complexity trade-off.
Year
DOI
Venue
2008
10.1109/GEFS.2008.4484560
2008 3RD INTERNATIONAL WORKSHOP ON GENETIC AND EVOLVING FUZZY SYSTEMS
Keywords
Field
DocType
bagging,genetic algorithm,boosting,learning artificial intelligence,fuzzy systems,curse of dimensionality,fuzzy classification,design methodology,cumulant,classification system,fuzzy set theory,knowledge based systems,evolutionary computation,genetic selection,scalability,genetic algorithms,machine learning
Data mining,Ensembles of classifiers,Fuzzy classification,Random subspace method,Fuzzy set,Artificial intelligence,Boosting (machine learning),Fuzzy control system,Machine learning,Genetic algorithm,Mathematics,Fuzzy rule
Conference
ISSN
Citations 
PageRank 
2373-0889
7
0.46
References 
Authors
18
3
Name
Order
Citations
PageRank
Oscar Cordón11572100.75
Arnaud Quirin216813.68
Sanchez, L.337723.74