Handling Concept Drift Using the Correlation between Multiple Data Streams
- Publication Type:
- Thesis
- Issue Date:
- 2022
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Concept Drift has been a major issue in handing streaming data in machine learning area. To date, the research on concept drift considers data streams separately, ignoring the correlations between data streams. Motivated by this, this research proposes four methods to deal with the correlations between data streams. A concept drift adaptation method is firstly proposed to overcome the insufficient training problem caused by scarce newly arrived data. By introducing correlation between multiple data streams, a multi-stream concept drift handling framework is then proposed to deal with concept drifting for multi-stream environment. Next, Evolutionary Regressor Chains are developed to track the correlations between multiple data streams. And lastly, a concept drift adaptation strategy for neural network classifiers is also developed for circumstances in which multiple data streams have different feature spaces. Extensive experiments have been conducted to evaluate the developed methods.
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