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Title: An adaptive fuzzy weight algorithm for the class imbalance learning problem

Authors: Vo Duc Quang; Tran Dinh Khang

Addresses: School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam; Faculty of Information Technology, Vinh University, Nghean, Vietnam ' School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam

Abstract: 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.

Keywords: classification algorithm; class imbalance learning; CIL; fuzzy support vector machines; FSVM; weighted support vector machines; WSVM; support vector machine; SVM.

DOI: 10.1504/IJIIDS.2024.137666

International Journal of Intelligent Information and Database Systems, 2024 Vol.16 No.3, pp.221 - 240

Received: 16 Dec 2022
Accepted: 13 Jul 2023

Published online: 02 Apr 2024 *

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