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The class imbalance problem is important and challenging. Ensemble approachesare widely used to tackle this problem because of their effectiveness. However,existing ensemble methods are always applied into original samples, while notconsidering the structure information among original samples. The limitationwill prevent the imbalanced learning from being better. Besides, research showsthat the structure information among samples includes local and globalstructure information. Based on the analysis above, an imbalanced ensemblealgorithm with the deep sample pre-envelope network (DSEN) and local-globalstructure consistency mechanism (LGSCM) is proposed here to solve theproblem.This algorithm can guarantee high-quality deep envelope samples forconsidering the local manifold and global structures information, which ishelpful for imbalance learning. First, the deep sample envelope pre-network(DSEN) is designed to mine structure information among samples.Then, the localmanifold structure metric (LMSM) and global structure distribution metric(GSDM) are designed to construct LGSCM to enhance distribution consistency ofinterlayer samples. Next, the DSEN and LGSCM are put together to form the finaldeep sample envelope network (DSEN-LG). After that, base classifiers areapplied on the layers of deep samples respectively.Finally, the predictiveresults from base classifiers are fused through bagging ensemble learningmechanism. To demonstrate the effectiveness of the proposed method, forty-fourpublic datasets and more than ten representative relevant algorithms are chosenfor verification. The experimental results show that the algorithm issignificantly better than other imbalanced ensemble algorithms.

Envelope imbalanced ensemble model with deep sample learning and local-global structure consistency