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Imbalanced learning is important and challenging since the problem of theclassification of imbalanced datasets is prevalent in machine learning and datamining fields. Sampling approaches are proposed to address this issue, andcluster-based oversampling methods have shown great potential as they aim tosimultaneously tackle between-class and within-class imbalance issues. However,all existing clustering methods are based on a one-time approach. Due to thelack of a priori knowledge, improper setting of the number of clusters oftenexists, which leads to poor clustering performance. Besides, the existingmethods are likely to generate noisy instances. To solve these problems, thispaper proposes a deep instance envelope network-based imbalanced learningalgorithm with the multilayer fuzzy c-means (MlFCM) and a minimum interlayerdiscrepancy mechanism based on the maximum mean discrepancy (MIDMD). Thisalgorithm can guarantee high quality balanced instances using a deep instanceenvelope network in the absence of prior knowledge. In the experimentalsection, thirty-three popular public datasets are used for verification, andover ten representative algorithms are used for comparison. The experimentalresults show that the proposed approach significantly outperforms other popularmethods.

Envelope Imbalance Learning Algorithm based on Multilayer Fuzzy C-means Clustering and Minimum Interlayer discrepancy