Imbalanced Datasets Classification by Fuzzy Rule Extraction and Genetic Algorithms

We propose a method based on the extraction of fuzzy rules by genetic algorithms 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 recombined to obtain 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