A method to classify data by fuzzy rule extraction from imbalanced datasets

Vicenç Soler, Jesus Cerquides, Josep Sabria, Jordi Roig, Marta Prim

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Resum

We propose a method based on fuzzy rules for the classification of imbalanced datasets when understandability is an issue. We propose a new method for fuzzy variable construction based on modifying the set of fuzzy variables obtained by the RecBF/DDA algorithm. Later, these variables are combined into fuzzy rules by means of a Genetic Algorithm. The method has been developed for the detection of Down's syndrome in fetus. We provide 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.

Idioma originalEnglish
Títol de la publicacióArtificial Intelligence Research and Development
EditorIOS Press BV
Pàgines55-62
Nombre de pàgines8
ISBN (imprès)1586036637, 9781586036638
Estat de la publicacióPublicada - 2006

Sèrie de publicacions

NomFrontiers in Artificial Intelligence and Applications
Volum146
ISSN (imprès)0922-6389
ISSN (electrònic)1879-8314

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