Imbalanced classification in sparse and large behaviour datasets

Recent years have witnessed a growing number of publications dealing with the imbalanced learning issue. While a plethora of techniques have been investigated on traditional low-dimensional data, little is known on the effect thereof on behaviour data. This kind of data reflects fine-grained behaviours of individuals or organisations and is characterized by sparseness and very large dimensions. In this article, we investigate the effects of several over-and undersampling, cost-sensitive learning and boosting techniques on the problem of learning from imbalanced behaviour data. Oversampling techniques show a good overall performance and do not seem to suffer from overfitting as traditional studies report. A variety of undersampling approaches are investigated as well and show the performance degrading effect of instances showing odd behaviour. Furthermore, the boosting process indicates that the regularization parameter in the SVM formulation acts as a weakness indicator and that a combination of weak learners can often achieve better generalization than a single strong learner. Finally, the EasyEnsemble technique is presented as the method outperforming all others. By randomly sampling several balanced subsets, feeding them to a boosting process and subsequently combining their hypotheses, a classifier is obtained that achieves noise/outlier reduction effects and simultaneously explores the majority class space efficiently. Furthermore, the method is very fast since it is parallelizable and each subset is only twice as large as the minority class size.