  Imbalanced learning is important and challenging since the problem of the
classification of imbalanced datasets is prevalent in machine learning and data
mining fields. Sampling approaches are proposed to address this issue, and
cluster-based oversampling methods have shown great potential as they aim to
simultaneously tackle between-class and within-class imbalance issues. However,
all existing clustering methods are based on a one-time approach. Due to the
lack of a priori knowledge, improper setting of the number of clusters often
exists, which leads to poor clustering performance. Besides, the existing
methods are likely to generate noisy instances. To solve these problems, this
paper proposes a deep instance envelope network-based imbalanced learning
algorithm with the multilayer fuzzy c-means (MlFCM) and a minimum interlayer
discrepancy mechanism based on the maximum mean discrepancy (MIDMD). This
algorithm can guarantee high quality balanced instances using a deep instance
envelope network in the absence of prior knowledge. In the experimental
section, thirty-three popular public datasets are used for verification, and
over ten representative algorithms are used for comparison. The experimental
results show that the proposed approach significantly outperforms other popular
methods.
