A fast genetic method for inducting linguistically understandable fuzzy models

Author	L. Sánchez
Keywords	Artificial intelligence backfitting boosting techniques computational linguistics extended additive models fast genetic method fuzzy logic fuzzy models Fuzzy reasoning fuzzy rule bases fuzzy rule learning methods fuzzy set theory Fuzzy sets genetic algorithm genetic algorithms inference mechanisms learning (artificial intelligence) Learning systems linguistically understandable fuzzy model induction mixtures of experts reasoning methods residuals submodel fitting Training data uncertainty handling
Abstract	Fuzzy rule bases can be regarded as mixtures of experts, and boosting techniques can be applied to learn them from data. In particular, provided that adequate reasoning methods are used, fuzzy models are extended additive models, thus backfitting can be applied to them. We propose to use an implementation of backfitting that uses a genetic algorithm for fitting submodels to residuals and we also show that it is both more accurate and faster than other fuzzy rule learning methods.
Year of Publication	2001
Date Published	July
DOI	10.1109/NAFIPS.2001.943781
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Number of Pages	1559-1563

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