  The class imbalance problem is important and challenging. Ensemble approaches
are widely used to tackle this problem because of their effectiveness. However,
existing ensemble methods are always applied into original samples, while not
considering the structure information among original samples. The limitation
will prevent the imbalanced learning from being better. Besides, research shows
that the structure information among samples includes local and global
structure information. Based on the analysis above, an imbalanced ensemble
algorithm with the deep sample pre-envelope network (DSEN) and local-global
structure consistency mechanism (LGSCM) is proposed here to solve the
problem.This algorithm can guarantee high-quality deep envelope samples for
considering the local manifold and global structures information, which is
helpful for imbalance learning. First, the deep sample envelope pre-network
(DSEN) is designed to mine structure information among samples.Then, the local
manifold structure metric (LMSM) and global structure distribution metric
(GSDM) are designed to construct LGSCM to enhance distribution consistency of
interlayer samples. Next, the DSEN and LGSCM are put together to form the final
deep sample envelope network (DSEN-LG). After that, base classifiers are
applied on the layers of deep samples respectively.Finally, the predictive
results from base classifiers are fused through bagging ensemble learning
mechanism. To demonstrate the effectiveness of the proposed method, forty-four
public datasets and more than ten representative relevant algorithms are chosen
for verification. The experimental results show that the algorithm is
significantly better than other imbalanced ensemble algorithms.
