Ensemble of Cost-Sensitive Hypernetworks for Class-Imbalance Learning

Hyper network is a probabilistic graphic model of learning and memory inspired by biomolecular networks, which is very useful for discovering higher-order correlations among multiple attributes. However, as many traditional machine learning algorithms, hyper networks may bias towards the majority class, thus producing poor predictive accuracy over the minority class when learining with imbalacned datasets. In this paper, three hyper network-based models, namely ensemble of cost-sensitive hyper networks (EN-CS-HN), ensemble of cost-sensitive hyper networks with under-sampling (EN-CS-HN-UNDE), and ensemble of cost-sensitive hyper networks with synthetic minority over-sampling technique (EN-CS-HN-SMOTE) are proposed respectively. To examine the performance of the proposed schemes, we conduct experiments on ten imbalanced datasets collected from UCI machine learning repository, wherein the proposed methods are compared with various state-of-the-art approaches using three metrics: G-Mean, F-Measure and area under the receiver operating characteristic curve (AUC-ROC). Experimental results show that the proposed methods are able to surpass or match the previously known best algorithms on most of the ten datasets.