A Method to Classify Data by Fuzzy Rule Extraction from Imbalanced Datasets

• Autores: Vicenç Soler Ruiz, Jesús Cerquides Bueno , Josep Sabria, Jordi Roig de Zárate , Marta Prim Sabrià
• Localización: Artificial intelligence research and development / coord. por Beatriz López, Joaquim Meléndez Frigola , Petia Radeva Ivanova , Jordi Vitrià Marca , 2005, ISBN 978-1-58603-663-8, págs. 55-62
• Idioma: inglés
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• Resumen

  • 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.

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