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- W2255437727 abstract "The knowledge hidden in evolving data may change with time, this issue is known as concept drift. It often causes a learning system to decrease its prediction accuracy. Most existing techniques apply ensemble methods to improve learning performance on concept drift. In this paper, we propose a novel meta learning approach for this issue and develop a method: Multi-Step Learning (MSL). In our method, a MSL learner is structured in a recursive manner, which contains all the base learners maintained in a hierarchy, ensuring the learned concepts are traceable. We evaluated MSL and two ensemble techniques on three synthetic datasets, which contain a number of drastic concept drifts. The experimental results show that the proposed method generally performs better than the ensemble techniques in terms of prediction accuracy." @default.
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- W2255437727 date "2010-01-01" @default.
- W2255437727 modified "2023-09-24" @default.
- W2255437727 title "A META-LEARNING METHOD FOR CONCEPT DRIFT" @default.
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- W2255437727 doi "https://doi.org/10.5220/0003095502570262" @default.
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