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Online concept evolution detection based on active learning

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Abstract

Concept evolution detection is an important and difficult problem in streaming data mining. When the labeled samples in streaming data insufficient to reflect the training data distribution, it will often further restrict the detection performance. This paper proposed a concept evolution detection method based on active learning (CE_AL). Firstly, the initial classifiers are constructed by a small number of labeled samples. The sample areas are divided into the automatic labeling and the active labeling areas according to the relationship between the classifiers of different categories. Secondly, for online new coming samples, according to their different areas, two strategies based on the automatic learning-based model labeling and active learning-based expert labeling are adopted respectively, which can improve the online learning performance with only a small number of labeled samples. Besides, the strategy of “data enhance” combined with “model enhance” is adopted to accelerate the convergence of the evolution category detection model. The experimental results show that the proposed CE_AL method can enhance the detection performance of concept evolution and realize efficient learning in an unstable environment by labeling a small number of key samples.

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Acknowledgements

This research was partially supported by the National Natural Science Foundation of China (Nos. 62276157, U21A20513, 62076154, 61503229), the Key R &D Program of Shanxi Province (No. 202202020101003).

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Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Husheng Guo, Hai Li, Lu Cong & Wenjian Wang

  2. Key Lab of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, Shanxi, China

    Husheng Guo & Wenjian Wang

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  1. Husheng Guo
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Correspondence to Wenjian Wang.

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Guo, H., Li, H., Cong, L. et al. Online concept evolution detection based on active learning. Data Min Knowl Disc (2024). https://doi.org/10.1007/s10618-024-01011-4

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