Manifold neighboring envelope sample generation mechanism for imbalanced ensemble classification

For existing imbalanced ensemble (IE) methods, the sample subsets are constructed from the same dataset, which usually suffer from low quality (diversity and separability) of the subsets, so a manifold neighboring envelope sample generation mechanism (MNESG) and an imbalanced ensemble algorithm based on the mechanism (MNESG-IE) are proposed to solve this problem. First, for the original balanced subsets (OBS), a manifold neighboring sample envelope projection mechanism (MNSEP) is designed to mine the local correlation information between the samples and their nearest neighbors in the subsets. Second, the fuzzy c-means clustering (FCM) is used to further mine the global correlation information among similar samples in the subsets. Third, the sample distribution consistency preservation mechanism (SDCPM) is designed to enhance the consistency of the sample distribution before and after clustering. To better reduce the three accumulated losses above, the three steps are conducted simultaneously, thereby realizing the MNESG, which can transform the OBS into two new types of high quality envelope sample subsets - neighboring envelope sample (NES) subsets and neighboring cluster envelope sample (NCES) subsets. Finally, base classifiers are trained on the NES subsets and NCES subsets, and then fused by a two-dimensional sparse fusion mechanism (2D-SFM). Various representative IE algorithms on over thirty benchmark datasets are considered for verification. The results show that compared with the state-of-the-art IE algorithms, MNESG-IE achieves 17.79%, 17.90%, 23.61%, 18.08% improvement in terms of ACC, AUC, F-M and G-M, respectively. The major originality of the paper is: (a) proposing the MNSEP to mine the local correlation information for improving the quality of the subsets; (b) proposing the MNESG to generate high quality subsets by mining local and global correlation information simultaneously; and (c)forming an IE algorithm to better solve the imbalanced classification problem.