Pages 75-80
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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.
Keywords
- Bagging
- 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
Details
- Date Published
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March