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Learning efficient rulesets from fuzzy data with a genetic algorithm

  • Francisco Botana
Plasticity Phenomena (Maturing, Learning & Memory)
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1606)

Abstract

The aim of this paper is to demonstrate the feasibility of fuzzy measures of subsethood in learning from examples. Using the relationship between (fuzzy) set containment and (fuzzy) logical implication, a method of generating if-then rules that describe a fuzzy dataset is given. In order to obtain an efficient subset of the generated rules, we apply a simple genetic algorithm.

The proposed method is illustrated with a fuzzified well-known learning set. The results on this set clearly improve other approaches.

Keywords

Genetic Algorithm Fuzzy Rule Fuzzy Measure Fuzzy Data Simple Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Francisco Botana
    • 1
  1. 1.Departamento de Matemática AplicadaUniversidad de VigoPontevedraSpain

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