Introducing a genetic fuzzy linguistic combination method for bagging fuzzy rule-based multiclassification systems

TitleIntroducing a genetic fuzzy linguistic combination method for bagging fuzzy rule-based multiclassification systems
Publication TypeConference Paper
Year of Publication2010
AuthorsSánchez, L., Cordón O., Quirin A., and Trawinski K.
Conference Name2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)
Pagination75-80
Date PublishedMarch
KeywordsBagging, bagging fuzzy multiclassification system, Classification tree analysis, classifier ensemble, Computer science, Decision trees, fuzzy aggregation operators, fuzzy linguistic combination methods, fuzzy linguistic rule-based system, Fuzzy reasoning, fuzzy set theory, Fuzzy systems, genetic algorithms, genetic fuzzy-based system, Genetics, knowledge based systems, learning (artificial intelligence), machine learning, Neural networks, pattern classification
Abstract

Many different fuzzy aggregation operators have been successfully used to combine the outputs provided by the individual classifiers in a multiclassification system. However, up to our knowledge, the use of fuzzy combination methods composed of a fuzzy system is less extended. By using a fuzzy linguistic rule-based classification system as a combination method, the resulting classifier ensemble would show a hierarchical structure and the operation of the latter component would be transparent to the user. Moreover, for the specific case of fuzzy multiclassification systems, the new approach could also become a smart way to allow fuzzy classifiers to deal with high dimensional problems avoiding the curse of dimensionality. The present contribution establishes the first basis in this direction by introducing a genetic fuzzy system-based framework to build the fuzzy linguistic combination method for a bagging fuzzy multiclassification system.

DOI10.1109/GEFS.2010.5454152