Neural network with absent minority class samples and boundary shifting for imbalanced data classification

Neural networks handling data imbalance heavily rely on resampling or reweighting strategies. However, existing resampling and reweighting approaches mainly focus on rebalancing known data, which ignore the essence of the data imbalance problem, namely, the problem of insufficient empirical representation of the minority class caused by the small number of samples. Therefore, we propose a new solution for neural networks classifying imbalanced data by sampling absent minority class samples. Specifically, an improved Metropolis Hasting (IMH) algorithm is developed to sample absent minority class samples by collecting samples rejected by the majority class approximation process. The sampled absent minority samples are then provided to neural networks to address the data imbalance problem. For IMH, in order to accelerate the sampling process and reduce the vague class definition of the sampled minority class samples, line segment transition kernel and class probability constraint are proposed. For neural networks, two boundary shifting strategies are supported to operate on different application modes of sampled absent minority class samples. In experiments, the proposed method is validated on 34 imbalanced datasets. Comparable AUC, G-MEAN, and MACC results are achieved. These results demonstrate the effectiveness of sampling absent minority class samples for neural networks solving the imbalanced data problem.