A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in application of Evolutionary Algorithms to induce classification rules. This hybrid approach can aid in areas that classical methods to rule induction have not been completely successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when some classes heavily outnumbers other classes. Frequently, classical Machine Learning classifiers are not able to learn in the presence of imbalanced data sets, outputting classifiers that always predict the most numerous classes. In this work we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are given to classical Machine Learning systems that output rule sets. The rule sets are combined in a pool of rules and an evolutionary algorithm is used to build a classifier from this pool of rules. This hybrid approach has some advantages over under-sampling since it reduces the amount of discarded information, and some advantages over over-sampling since it avoids overfitting. This approach was experimentally analyzed and our experiments show the proposed approach can improve classification measured as the area under the ROC curve.