Facing imbalanced classes through aggregation of classifiers

Two class classification problems in real world are often characterized by imbalanced classes. This is a serious issue since a classifier trained on such a data distribution typically exhibits a prediction accuracy highly skewed towards the majority class. To improve the quality of the classifier, many approaches have been proposed till now for building artificially balanced training sets. Such methods are mainly based on undersampling the majority class and/or oversampling the minority class. However, both approaches can produce overfitting or underfilling problems for the trained classifier. In this paper we present a method for building a multiple classifier system in which each constituting classifier is trained on a subset of the majority class and on the whole minority class. The approach has been tested on the detection of microcalcifications on digital mammograms. The results obtained confirm the effectiveness of the method.