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

TL;DR

This paper introduces an innovative ensemble learning method that leverages deep sample pre-envelopes and local-global structure consistency to improve class imbalance handling, demonstrating superior performance over existing algorithms.

Contribution

The paper proposes a novel imbalanced ensemble algorithm combining deep sample envelopes with local-global structure consistency, enhancing structure information utilization in imbalanced learning.

Findings

Outperforms 10+ relevant algorithms on 44 datasets

Effectively preserves local and global sample structures

Significantly improves classification accuracy in imbalanced scenarios

Abstract

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.