Adaptive Fusion Based Method for Imbalanced Data Classification - Liang, Zefeng; Wang, Huan; Yang, Kaixiang; Shi, Yifan (Frontiers in Neurorobotics, 2022)
The imbalance problem is widespread in real-world applications. When training a classifier on the imbalance datasets, the classifier is hard to learn an appropriate decision boundary, which causes unsatisfying classification performance. To deal with the imbalance problem, various ensemble algorithms are proposed. However, conventional ensemble algorithms do not consider exploring an effective feature subspace to further improve the performance. In addition, they treat the base classifiers equally, and ignore the different contribution of each base classifier to the ensemble result. In order to address these problems, we propose a novel ensemble algorithm that combines effective data transform and adaptive fusion scheme. First, we utilize modified metric learning to obtain an effective feature space based on imbalanced data. Next, the base classifiers are assigned different weights adaptively. The experiments on multiple imbalanced datasets, including images and biomedical dataset, verify the superiority of our proposed ensemble algorithm.