Paper Info
Title
Bi-Criteria Genetic Selection Of Bagging Fuzzy Rule-Based Multiclassification Systems
Abstract
Previously we proposed a scheme to generate fuzzy rule-based multiclassification systems by means of bagging, mutual information-based feature selection, and a multicriteria genetic algorithm (GA) for static component classifier selection guided by the ensemble training error. In the current contribution we extend the latter component by the use of two bi-criteria fitness functions, combining the latter error measure with the selected ensemble likelihood. A study on four popular UCI datasets with different dimensionalities is conducted in order to analyze the accuracy-complexity trade-off obtained by the two GAs, the initial fuzzy ensemble and a single fuzzy classifier.
Year
Venue
Keywords
2009
PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE
Bagging, feature selection, fuzzy rule-based multiclassification systems, genetic selection of individual classifiers, multicriteria genetic algorithm
Field
DocType
Citations 
Fuzzy classification,Pattern recognition,Feature selection,Fuzzy logic,Artificial intelligence,Mutual information,Fuzzy classifier,Classifier (linguistics),Machine learning,Mathematics,Fuzzy rule
Conference
3
PageRank 
References 
Authors
0.38
16
3
Name
Order
Citations
PageRank
Krzysztof Trawiński124716.06
Arnaud Quirin216813.68
Oscar Cordón31572100.75