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Intensive Class Imbalance Learning in Drifting Data Streams
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AI
DOI：10.1109/TETCI.2024.3399657.png)
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Streaming data analysis faces two primary challenges: concept drifts and class imbalance. The co-occurrence of virtual drifts and class imbalance is a common real-world scenario requiring dedicated solutions. This paper presents Intensive Class Imbalance Learning (ICIL), a novel supervised classification method for virtually drifting data streams. ICIL facilitates the detection of virtual drifts through a feature-sensitive change detection method. It calibrates the data over time to resolve within-class imbalance, overlaps, and small sample size problems. A weighted voting ensemble is proposed for enhanced performance, wherein weights are constantly updated based on the recent performance of the member classifiers. Experiments are conducted on 14 synthetic and real-world data streams to demonstrate the efficacy of the proposed method. The comparative analysis against 11 state-of-the-art methods shows that the proposed method outperforms the other methods in 9/14 data streams on the G-mean metric.
Keyword:
Streams
Feature extraction
Data models
Monitoring
Ensemble learning
Power capacitors
Detectors
Class imbalance
classification
concept drift
data streams
ensemble learning
期刊
I
IF:
6.5
论文数:
1.4K
被引数:
4.5K
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