Adaptive heterogeneous ensemble learning using the context of test instances

We consider binary classification problems where each of the two classes shows a multi-modal distribution in the feature space, and the classification has to be performed over different test scenarios, where every test scenario only involves a subset of the positive and negative modes in the data. In such conditions, there may exist certain pairs of positive and negative modes, termed as pairs of confusing modes, which may not appear together in the same test scenario but can be highly overlapping in the feature space. Determining the class labels at such pairs of confusing modes is challenging as the labeling decisions depend not only on the feature values but also on the context of the test scenario. To overcome this challenge, we present the Adaptive Heterogeneous Ensemble Learning (AHEL) algorithm, which constructs an ensemble of classifiers in accordance with the multi-modality within the classes, and further assigns adaptive weights to classifiers based on their relevance in the context of a test scenario. We demonstrate the effectiveness of our approach in comparison with baseline approaches on a synthetic dataset and a real-world application involving global water monitoring.