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Volume 17
Issue 2
10.3390/sym17020182
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Multi-Label Learning with Distribution Matching Ensemble: An Adaptive and Just-In-Time Weighted Ensemble Learning Algorithm for Classifying a Nonstationary Online Multi-Label Data Stream

by
Chao Shen
1,†,
Bingyu Liu
1,†,
Changbin Shao
1,
Xibei Yang
1,
Sen Xu
2,
Changming Zhu
3 and
Hualong Yu
1,*
1
School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
2
School of Information Technology, Yancheng Institute of Technology, Yancheng 224051, China
3
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China
*
Author to whom correspondence should be addressed.
†
The authors contributed equally to this work.
Symmetry 2025, 17(2), 182; https://doi.org/10.3390/sym17020182
Submission received: 5 December 2024 / Revised: 21 January 2025 / Accepted: 22 January 2025 / Published: 24 January 2025
(This article belongs to the Section Computer)
Versions Notes

Abstract

Learning from a nonstationary data stream is challenging, as a data stream is generally considered to be endless, and the learning model is required to be constantly amended for adapting the shifting data distributions. When it meets multi-label data, the challenge would be further intensified. In this study, an adaptive online weighted multi-label ensemble learning algorithm called MLDME (multi-label learning with distribution matching ensemble) is proposed. It simultaneously calculates both the feature matching level and label matching level between any one reserved data block and the new received data block, further providing an adaptive decision weight assignment for ensemble classifiers based on their distribution similarities. Specifically, MLDME abandons the most commonly used but not totally correct underlying hypothesis that in a data stream, each data block always has the most approximate distribution with that emerging after it; thus, MLDME could provide a just-in-time decision for the new received data block. In addition, to avoid an infinite extension of ensemble classifiers, we use a fixed-size buffer to store them and design three different dynamic classifier updating rules. Experimental results for nine synthetic and three real-world multi-label nonstationary data streams indicate that the proposed MLDME algorithm is superior to some popular and state-of-the-art online learning paradigms and algorithms, including two specifically designed ones for classifying a nonstationary multi-label data stream.
Keywords: multi-label data stream; adaptive weighted ensemble; concept drift; distribution matching; Gaussian mixture model; Kullback–Leibler divergence; label distribution drift detection multi-label data stream; adaptive weighted ensemble; concept drift; distribution matching; Gaussian mixture model; Kullback–Leibler divergence; label distribution drift detection

Share and Cite

MDPI and ACS Style

Shen, C.; Liu, B.; Shao, C.; Yang, X.; Xu, S.; Zhu, C.; Yu, H. Multi-Label Learning with Distribution Matching Ensemble: An Adaptive and Just-In-Time Weighted Ensemble Learning Algorithm for Classifying a Nonstationary Online Multi-Label Data Stream. Symmetry 2025, 17, 182. https://doi.org/10.3390/sym17020182

AMA Style

Shen C, Liu B, Shao C, Yang X, Xu S, Zhu C, Yu H. Multi-Label Learning with Distribution Matching Ensemble: An Adaptive and Just-In-Time Weighted Ensemble Learning Algorithm for Classifying a Nonstationary Online Multi-Label Data Stream. Symmetry. 2025; 17(2):182. https://doi.org/10.3390/sym17020182

Chicago/Turabian Style

Shen, Chao, Bingyu Liu, Changbin Shao, Xibei Yang, Sen Xu, Changming Zhu, and Hualong Yu. 2025. "Multi-Label Learning with Distribution Matching Ensemble: An Adaptive and Just-In-Time Weighted Ensemble Learning Algorithm for Classifying a Nonstationary Online Multi-Label Data Stream" Symmetry 17, no. 2: 182. https://doi.org/10.3390/sym17020182

APA Style

Shen, C., Liu, B., Shao, C., Yang, X., Xu, S., Zhu, C., & Yu, H. (2025). Multi-Label Learning with Distribution Matching Ensemble: An Adaptive and Just-In-Time Weighted Ensemble Learning Algorithm for Classifying a Nonstationary Online Multi-Label Data Stream. Symmetry, 17(2), 182. https://doi.org/10.3390/sym17020182

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