|Title||Using the Adaboost algorithm for extracting fuzzy rules from low quality data: Some preliminary results|
|Publication Type||Conference Paper|
|Year of Publication||2011|
|Authors||Palacios, A. M., Sánchez L., and Couso I.|
|Conference Name||2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)|
|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|
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.