Paper Info
Title
On the Combination of Accuracy and Diversity Measures for Genetic Selection of Bagging Fuzzy Rule-Based Multiclassification Systems
Abstract
A preliminary study combining two diversity measures with an accuracy measure in two bicriteria fitness functions to genetically select fuzzy rule-based multiclassification systems is conducted in this paper. The fuzzy rule-based classification system ensembles are generated by means of bagging and mutual information-based feature selection. Several experiments were developed using four popular UCI datasets with different dimensionality in order to analyze the accuracy-complexity trade-off obtained by a genetic algorithm considering the two fitness functions. Comparison are made with the initial fuzzy ensemble and a single fuzzy classifier.
Year
DOI
Venue
2009
10.1109/ISDA.2009.123
ISDA
Keywords
Field
DocType
fuzzy rule-based classification system,single fuzzy classifier,genetic selection,bicriteria fitness function,fitness function,fuzzy rule-based multiclassification system,diversity measures,accuracy measure,initial fuzzy ensemble,accuracy-complexity trade-off,different dimensionality,diversity measure,bagging fuzzy rule-based multiclassification,mutual information,feature selection,genetic algorithm,genetic algorithms,classification system,bagging,fuzzy set theory
Data mining,Feature selection,Fuzzy classification,Computer science,Fuzzy set,Artificial intelligence,Genetic algorithm,Neuro-fuzzy,Pattern recognition,Fuzzy logic,Mutual information,Machine learning,Fuzzy rule
Conference
ISSN
ISBN
Citations 
2164-7143
978-0-7695-3872-3
6
PageRank 
References 
Authors
0.44
19
3
Name
Order
Citations
PageRank
Krzysztof Trawiński124716.06
Arnaud Quirin216813.68
Oscar Cordón31572100.75