Ensemble Learning Methods for Binary Classification with Multi-modality within the Classes

Abstract

We consider binary classification problems where each of the two classes show multi-modal distribution in the feature space. Inspired by existing ensemble learning methods for multi-class classification, we develop ensemble learning methods for binary classification that make use of the bipartite nature of the positive and negative modes in the data. By constructing ensembles that make use of the multi-modal structure within the two classes, as opposed to using random samples, we are able to ensure sufficient diversity among the classifiers and adequate representation of the modes in the learning of the classifiers. We demonstrate the effectiveness of the proposed ensemble learning methods in comparison with existing approaches over a synthetic dataset and a real-world application involving global lake monitoring, over a broad range of base classifiers.