An adaptive fuzzy weight algorithm for the class imbalance learning problem

In this study, we propose an adaptive fuzzy weight algorithm for the problem of two-class imbalanced learning. Initially, our algorithm finds a set of fuzzy weight values for data samples based on the distance from each sample to the centres of both minority and majority classes. Then, our algorithm iteratively adjusts the fuzzy weight values of sensitive samples on either positive or negative margins or class label noises. By doing so, our algorithm increases the influence of minority samples and decreases the influence of majority samples in forming a classifier model. Experimental results on four benchmark real-world imbalanced datasets including Transfusion, Ecoli, Yeast, and Abalone show that our algorithm outperforms the fuzzy SVM-CIL algorithm in terms of classification performance. © 2024 Inderscience Enterprises Ltd.