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An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift

Authors :
Meng Han
Chunpeng Li
Fanxing Meng
Feifei He
Ruihua Zhang
Source :
Applied Sciences, Vol 14, Iss 16, p 7176 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Learning from multiclass imbalanced data streams with concept drift and variable class imbalance ratios under a limited label budget presents new challenges in the field of data mining. To address these challenges, this paper proposes an adaptive active learning method for multiclass imbalanced data streams with concept drift (AdaAL-MID). Firstly, a dynamic label budget strategy under concept drift scenarios is introduced, which allocates label budgets reasonably at different stages of the data stream to effectively handle concept drift. Secondly, an uncertainty-based label request strategy using a dual-margin dynamic threshold matrix is designed to enhance learning opportunities for minority class instances and those that are challenging to classify, and combined with a random strategy, it can estimate the current class imbalance distribution by accessing only a limited number of instance labels. Finally, an instance-adaptive sampling strategy is proposed, which comprehensively considers the imbalance ratio and classification difficulty of instances, and combined with a weighted ensemble strategy, improves the classification performance of the ensemble classifier in imbalanced data streams. Extensive experiments and analyses demonstrate that AdaAL-MID can handle various complex concept drifts and adapt to changes in class imbalance ratios, and it outperforms several state-of-the-art active learning algorithms.

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
16
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.6b81173394e944318a6fdfbdaf8ea8d0
Document Type :
article
Full Text :
https://doi.org/10.3390/app14167176