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.