Adaptive Fusion Based Method for Imbalanced Data Classification

Abstract

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 space to further improve the performance. In addition, they treat the base classifiers equally and ignore the different contributions of each base classifier to the ensemble result. In order to address these problems, we propose a novel ensemble algorithm that combines effective data transformation and an adaptive weighted voting 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 datasets verify the superiority of our proposed ensemble algorithm.

Performance analysis of samplers and calibrators with various classifiers for asymmetric hydrological data

“…Models constructed with bias from asymmetric datasets yield unreliable predictions and unsatisfactory classification results [7]. and applicable models for forecasting asymmetric data [9,10]. Asymmetric data used for prediction often hampers classifier performance and leads to various issues, including misclassification, under fitting, and exaggerated results.…”