Abstract
Many times in classification problems, particularly in critical real world applications, one of the classes has much less samples than the others (usually known as the class imbalance problem). In this work we discuss and evaluate the use of the REPMAC algorithm to solve imbalanced problems. Using a clustering method, REPMAC recursively splits the majority class in several subsets, creating a decision tree, until the resulting sub-problems are balanced or easy to solve. We use two diverse clustering methods and three different classifiers coupled with REPMAC to evaluate the new method on several benchmark datasets spanning a wide range of number of features, samples and imbalance degree. We also apply our method to a real world problem, the identification of weed seeds. We find that the good performance of REPMAC is almost independent of the classifier or the clustering method coupled to it, which suggests that its success is mostly related to the use of an appropriate strategy to cope with imbalanced problems.