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

Vicenç Soler*, Marta Prim

*Autor correspondiente de este trabajo

Producción científica: Capítulo del libroCapítuloInvestigaciónrevisión exhaustiva

3 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaMining Complex Data
EditoresDjamel Zighed, Hakim Hacid, Shusaku Tsumoto, Zbigniew Ras
Páginas23-39
Número de páginas17
DOI
EstadoPublicada - 2009

Serie de la publicación

NombreStudies in Computational Intelligence
Volumen165
ISSN (versión impresa)1860-949X

Huella

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