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Ensemble learning-based online sequential pre-interference extreme learning for concept drifting and class imbalanced
Yinjie Huang1, Hui Wen2, Qunhua Tang1
1College of Intelligent Manufacturing, Putian University, Putian, Fujian, China.
Plos One
|July 31, 2026
Summary
This study introduces an online sequential pre-interference layer extreme learning machine (OS-PIELM) to tackle concept drift and class imbalance in real-time data streams, enhancing model performance and robustness.
Area of Science:
- Machine Learning
- Data Science
- Artificial Intelligence
Background:
- Real-time data streams often suffer from concept drift and class imbalance.
- Existing methods struggle to effectively address both challenges simultaneously.
Purpose of the Study:
- To propose a novel online sequential pre-interference layer extreme learning machine (OS-PIELM).
- To enhance the model's ability to handle concept drift and class imbalance in data streams.
Main Methods:
- Introduced a pre-interference layer to improve nonlinear feature representation.
- Incorporated an adaptive forgetting factor and Gmean-based concept drift detection.
- Utilized a dynamic weighting strategy and an online ensemble learning framework.
Main Results:
- The proposed OS-PIELM effectively addresses class imbalance in data streams.
- Improved sensitivity and performance in concept drift detection.
- Demonstrated enhanced robustness through extensive experiments on synthetic and real-world datasets.
Conclusions:
- OS-PIELM offers a robust solution for real-time data stream analysis with concept drift and class imbalance.
- The integrated components significantly improve model adaptability and discriminative power.
- The ensemble framework further boosts overall performance and reliability.
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