Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms

Abstract

Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzy-valued data. These algorithms are efficient when the observation error is small. This paper is about datasets with medium to high discrepancies between the observed and the actual values of the variables, such as those containing missing values and… (More)
DOI: 10.1109/FUZZY.2007.4295659

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Cite this paper

@article{Snchez2007LearningFL, title={Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms}, author={Luciano S{\'a}nchez and Jos{\'e} M Otero}, journal={2007 IEEE International Fuzzy Systems Conference}, year={2007}, pages={1-6} }