Model-Based Oversampling for Imbalanced Sequence Classification

Sequence classification is critical in the data mining communities. It becomes more challenging when the class distribution is imbalanced, which occurs in many real-world applications. Oversampling algorithms try to re-balance the skewed class by generating synthetic data for minority classes, but most of existing oversampling approaches could not consider the temporal structure of sequences, or handle multivariate and long sequences. To address these problems, this paper proposes a novel oversampling algorithm based on the 'generative' models of sequences. In particular, a recurrent neural network was employed to learn the generative mechanics for sequences as representations for the corresponding sequences. These generative models are then utilized to form a kernel to capture the similarity between different sequences. Finally, oversampling is performed in the kernel feature space to generate synthetic data. The proposed approach can handle highly imbalanced sequential data and is robust to noise. The competitiveness of the proposed approach is demonstrated by experiments on both synthetic data and benchmark data, including univariate and multivariate sequences.