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Learning from Imbalanced Data Streams Using Rotation-Based Ensemble Classifiers

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Computational Collective Intelligence (ICCCI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14162))

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Abstract

In this paper, the problem of learning from imbalanced data streams is considered. To solve this problem, an approach is presented based on the processing of data chunks, which are formed using over-sampling and under-sampling. The final classification output is determined using an ensemble approach, which is supported by the rotation technique to introduce more diversification into the pool of base classifiers and increase the final performance of the system. The proposed approach is called Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI). It is validated experimentally using several selected benchmarks, and some results are presented and discussed. The paper concludes with a discussion of future research directions.

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Notes

  1. 1.

    In [4], it was shown that the use of ENN was superior to CNN, which held true for all of the datasets considered.

  2. 2.

    For OUOB, OB and Learn +  +.NIE, the Hoeffding tree was used for the base classifiers, and the base classifier pool was set to 10.

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Authors and Affiliations

  1. Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Ireneusz Czarnowski

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  1. Ireneusz Czarnowski
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Correspondence to Ireneusz Czarnowski .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Eötvös Loránd University, Budapest, Hungary

    János Botzheim

  3. Eötvös Loránd University, Budapest, Hungary

    László Gulyás

  4. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  5. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  6. Universität Münster, Münster, Germany

    Gottfried Vossen

  7. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

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Czarnowski, I. (2023). Learning from Imbalanced Data Streams Using Rotation-Based Ensemble Classifiers. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_60

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  • DOI: https://doi.org/10.1007/978-3-031-41456-5_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41455-8

  • Online ISBN: 978-3-031-41456-5

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