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Open Access | Just Accepted

An Ensemble Learning Model Based on Three-Way Decision for Concept Drift Adaptation

Dayong Deng1Wenxin Shen2Zhixuan Deng2( )Tianrui Li3Anjin Liu4

1 Xingzhi Collage of Zhejiang Normal University, and also with the Zhejiang Key Laboratory of Intelligent Education Technology and Application of Zhejiang Normal University, Jinhua 321000, China

2 School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321000, China

3 School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China

4 Decision Systems and e-Service Intelligence Laboratory, University of Technology Sydney, Sydney, NSW 2007, Australia

 

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Abstract

The ensemble learning model can effectively detect drift and utilize diversity to improve the performance of adapting to drift. However, local concept drift can occur in different types at different time points, causing basic learners are difficult to distinguish the drift of local boundaries, and the drift range is difficult to determine. Thus, the ensemble learning model to adapt local concept drifts is still challenging problem. Moreover, there are often differences in decision boundaries after drift adaptation, and employing overall diversity measurement is inappropriate. To address these two issues, this paper proposes a novel ensemble learning model called instanceweighted ensemble learning based on the three-way decision (IWE-TWD). In IWE-TWD, a divide-and-conquer strategy is employed to handle uncertain drift and to select base learners; Density clustering dynamically constructs density regions to lock drift range; Three-way decision is adopted to estimate whether the region distribution changes, and the instance is weighted with the probability of region distribution change; The diversities between base learners are determined with three-way decision also. Experimental results show that IWE-TWD has better performance than the state-of-the-art models in data stream classification on ten synthetic data sets and seven real-world data sets.

Tsinghua Science and Technology
Cite this article:
Deng D, Shen W, Deng Z, et al. An Ensemble Learning Model Based on Three-Way Decision for Concept Drift Adaptation. Tsinghua Science and Technology, 2024, https://doi.org/10.26599/TST.2024.9010085

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Received: 12 January 2024
Revised: 24 March 2024
Accepted: 08 May 2024
Available online: 09 May 2024

© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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