A comprehensive active learning method for multiclass imbalanced data streams with concept drift

A challenge to many real-world applications is multiclass imbalance with concept drift. In this paper, we propose a comprehensive active learning method for multiclass imbalanced streaming data with concept drift (CALMID). First, we design a comprehensive online active learning framework that includ...

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Veröffentlicht in:Knowledge-based systems Jg. 215; S. 106778
Hauptverfasser: Liu, Weike, Zhang, Hang, Ding, Zhaoyun, Liu, Qingbao, Zhu, Cheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 05.03.2021
Elsevier Science Ltd
Schlagworte:
Active learning
Algorithms
Classifiers
Concept drift
Data stream
Data transmission
Drift
Imbalance
Learning
Machine learning
Multiclass imbalance
Online active learning
Sliding
Teaching methods
Thresholds
Uncertainty
Multiclass imbalance
Data stream
Online active learning
Concept drift
ISSN:0950-7051, 1872-7409
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Zusammenfassung:A challenge to many real-world applications is multiclass imbalance with concept drift. In this paper, we propose a comprehensive active learning method for multiclass imbalanced streaming data with concept drift (CALMID). First, we design a comprehensive online active learning framework that includes an ensemble classifier, a drift detector, a label sliding window, sample sliding windows and an initialization training sample sequence. Next, a variable threshold uncertainty strategy based on an asymmetric margin threshold matrix is designed to comprehensively address the problem that a given class can simultaneously be a majority to a given subset of classes while also being a minority to others. Last but not least, we design a novel sample weight formula that comprehensively considers the class imbalance ratio of the sample’s category and the prediction difficulty. On 10 multiclass synthetic streams with different imbalance ratios and concept drifts, and on 5 real-world imbalanced streams with 7 to 55 classes and unknown drifts, the experimental results demonstrate that the proposed CALMID is more effective and efficient than several state-of-the-art learning algorithms.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2021.106778