Concept drift detection based on radial distance
- Publisher:
- ELSEVIER
- Publication Type:
- Journal Article
- Citation:
- Neurocomputing, 2025, 653
- Issue Date:
- 2025-11-07
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With the advancement of powerful computational hardware in recent years, neural networks, once popular for machine learning on pre-collected datasets, are becoming applicable for streaming data processing. Stream data have the characteristics of high velocity, variety and volume. In stream data mining a common challenge is concept drift, which refers to the phenomenon where the statistical properties of the target variable in predictive tasks change over time. For instance, in image prediction problems, the input facilities may be altered by the environment or device flaws. The materials generated from them could be distorted, such as blurring, discoloring or part-missing. Concept drift problem is considered a root cause of performance degradation in machine learning models on stream data. Traditional concept drift detection methods usually require large amount of historical data, which leads to substantial memory footprint and high computational cost, and tend to be overly sensitive to arbitrary distribution changes not related to prediction results. Such limitations are particularly evident in settings using neural network models, where input data are usually high dimensional images or videos. Aiming to improve the accuracy and efficiency of concept drift detection in neural network models, we propose a new concept drift detection method that specifically addresses these limitations and is applicable to general neural network models. Our method represents the original data with a distance-based statistic, extracted from the layer outputs of the neural network models, and is able to adjust its sensitivity to input distribution changes based on their relevance to neural network features. We evaluated our method with popular neural network architectures on both synthetic and real-world data sets. The results showed our method not only outperforms existing concept drift methods in accuracy, but is also significantly faster and consumes less resources.
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