Paper Info
Title
Comparing two genetic overproduce-and-choose strategies for fuzzy rule-based multiclassification systems generated by bagging and mutual information-based feature selection
Abstract
In [14] 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 for static component classifier selection guided by the ensemble training error. In the current contribution we extend the latter component by making use of the bagging approach's capability to evaluate the accuracy of the classifier ensemble using the out-of-bag estimates. An exhaustive study is developed on the potential of the two multicriteria genetic algorithms respectively considering the classical training error and the out-of-bag error fitness functions to design a final multiclassifier with an appropriate accuracy-complexity trade-off. Several parameter settings for the global approach are tested when applied to nine popular UCI datasets with different dimensionality.
Year
DOI
Venue
2010
10.3233/HIS-2010-0104
Int. J. Hybrid Intell. Syst.
Keywords
Field
DocType
mutual information-based feature selection,classifier ensemble,multicriteria genetic algorithm,global approach,ensemble training error,out-of-bag error fitness function,fuzzy rule-based multiclassification system,classical training error,out-of-bag estimate,latter component,bagging approach,genetic overproduce-and-choose strategy,mutual information,fitness function,feature selection,genetics,genetic algorithm
Data mining,Pattern recognition,Feature selection,Computer science,Curse of dimensionality,Artificial intelligence,Mutual information,Classifier (linguistics),Genetic algorithm,Machine learning,Fuzzy rule
Journal
Volume
Issue
Citations 
7
1
16
PageRank 
References 
Authors
0.57
44
2
Name
Order
Citations
PageRank
Oscar Cordón11572100.75
Arnaud Quirin216813.68