Extracting a fuzzy system by using genetic algorithms for imbalanced datasets classification: Application on Down's syndrome detection

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Abstract

This chapter presents a new methodology to extract a Fuzzy System by using Genetic Algorithms for the classification of imbalanced datasets when the intelligibility of the Fuzzy Rules is an issue. We propose a method for fuzzy variable construction, based on modifying the set of fuzzy variables obtained by the DDA/RecBF clustering algorithm. Afterwards, these variables are recombined to obtain Fuzzy Rules by means of a Genetic Algorithm. The method has been developed for the prenatal Down's syndrome detection during the secondtrimester of pregnancy. We present empirical results showing its accuracy for this task. Furthermore, we provide more generic experimental results over UCI datasets proving that the method can have a wider applicability on imbalanced datasets. © 2009 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Pages (from-to)23-39
JournalStudies in Computational Intelligence
Volume165
DOIs
Publication statusPublished - 1 Jan 2009

Keywords

  • Down Syndrome
  • Fuzzy logic
  • Imbalanced Datasets
  • RecBF
  • Rule extraction

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