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Data stream classification with novel class detection: a review, comparison and challenges

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Abstract

Developing effective and efficient data stream classifiers is challenging for the machine learning community because of the dynamic nature of data streams. As a result, many data stream learning algorithms have been proposed during the past decades and achieve great success in various fields. This paper aims to explore a specific type of challenge in learning evolving data streams, called concept evolution (emergence of novel classes). Concept evolution indicates that the underlying patterns evolve over time, and new patterns (classes) may emerge at any time in streaming data. Therefore, data stream classifiers with emerging class detection have received increasing attention in recent years due to the practical values in many real-world applications. In this article, we provide a comprehensive overview of the existing works in this line of research. We discuss and analyze various aspects of the proposed algorithms for data stream classification with concept evolution detection and adaptation. Additionally, we discuss the potential application areas in which these techniques can be used. We also provide a detailed overview of evaluation measures and datasets used in these studies. Finally, we describe the current research challenges and future directions for data stream classification with novel class detection.

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Notes

  1. https://archive.ics.uci.edu/ml/datasets.php.

  2. http://users.rowan.edu/~polikar/nse.html.

  3. https://moa.cms.waikato.ac.nz/datasets/.

  4. http://mlkd.csd.auth.gr/concept_drift.html.

  5. http://www.cse.fau.edu/xqzhu/stream.html.

  6. https://lucykuncheva.co.uk/EPSRC_simulation_framework/changing_environments_stage1a.htm.

  7. http://www.cs.bham.ac.uk/+minkull/opensource.html.

  8. https://github.com/vlosing/driftDatasets.

  9. http://github.com/gditzler/ConceptDriftResources.

  10. http://roveri.faculty.polimi.it/software-and-datasets.

  11. http://dbpedia.org/page/Concept_drift.

  12. http://moa.cms.waikato.ac.nz/.

  13. http://jmlr.org/papers/v16/morales15a.html.

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Acknowledgements

This work is supported by the Fundamental Research Funds for the Central Universities (ZYGX 2019Z014), National Natural Science Foundation of China (61976044, 52079026), Fok Ying-Tong Education Foundation (161062) and Sichuan Science and Technology Program (2020 YFH0037).

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  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Salah Ud Din, Junming Shao, Jay Kumar, Cobbinah Bernard Mawuli & Qinli Yang

  2. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313001, China

    Salah Ud Din, Junming Shao, Jay Kumar, Cobbinah Bernard Mawuli & Qinli Yang

  3. COMSATS University Islamabad, Abbottabad Campus, Islamabad, Pakistan

    Salah Ud Din

  4. Computational Intelligence Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    S. M. Hasan Mahmud

  5. Science and Technology on Electronic Information Control Laboratory, Chengdu, China

    Wei Zhang

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  1. Salah Ud Din
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Din, S.U., Shao, J., Kumar, J. et al. Data stream classification with novel class detection: a review, comparison and challenges. Knowl Inf Syst 63, 2231–2276 (2021). https://doi.org/10.1007/s10115-021-01582-4

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