Coupling REPMAC with FDA to solve highly imbalanced classification problems

Abstract

In many critical real world classification problems one of the classes has much less samples than the others (class imbalance). In a previous work we introduced 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. In this work we evaluate the use of three different classifiers coupled with REPMAC. We compare the perfomance of those methods using 7 datasets from the UCI repository spanning a wide range of number of features and imbalance degree. We find that the good perfomance of REPMAC is almost independent of the classifier coupled to it, which suggest that it success is mostly related to the use of an appropriate strategy to cope with imbalanced problems