Pages 1263-1270
Cite this publication
Palacios, A. M., Sánchez, L., & Couso, I. (2011). Using the Adaboost algorithm for extracting fuzzy rules from low quality data: Some preliminary results. 1263-1270. https://doi.org/10.1109/FUZZY.2011.6007647
Abstract
When the Adaboost algorithm is used for extracting fuzzy rules from data, each rule is regarded as a weak learner, and knowledge bases as assimilated to ensembles. In this paper we propose an extension of this framework for obtaining fuzzy rule-based classifiers from imprecise data. In the new approach, the mentioned search of the best rule at each iteration is carried out by a genetic algorithm with a fuzzy fitness function. The instances will be assigned fuzzy weights, however each fuzzy rule will be associated to a crisp number of votes.
Keywords
- Adaboost algorithm
- Boosting
- data handling
- Electronic mail
- fuzzy fitness function
- fuzzy rule extraction
- fuzzy rule-based classifiers
- fuzzy set theory
- Fuzzy systems
- fuzzy weights
- genetic algorithm
- genetic algorithms
- Genetic Fuzzy Systems
- knowledge based systems
- knowledge bases
- learning (artificial intelligence)
- Low Quality Data
- Merging
- Optimization
- pattern classification
- Training
Details
- Date Published
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June