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Oversampling for Mining Imbalanced Datasets: Taxonomy and Performance Evaluation

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Computational Collective Intelligence (ICCCI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13501))

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Abstract

The paper focuses on methods and algorithms for oversampling two-classes imbalanced datasets. We propose a taxonomy for oversampling approaches and review state-of-the-art algorithms. The paper discusses also some strengths and weaknesses of the oversampling methods. A computational experiment aims at comparing the performance of several oversampling algorithms. Conclusions discuss possible directions for future developments in the field of balancing imbalanced datasets to achieve better performance when mining them.

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Author information

Authors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Piotr Jedrzejowicz

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  1. Piotr Jedrzejowicz
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Correspondence to Piotr Jedrzejowicz .

Editor information

Editors and Affiliations

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

    Ngoc Thanh Nguyen

  2. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  3. University of Pau and Pays de l'Adour, Anglet, France

    Richard Chbeir

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

    Adrianna Kozierkiewicz

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

    Bogdan Trawiński

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Jedrzejowicz, P. (2022). Oversampling for Mining Imbalanced Datasets: Taxonomy and Performance Evaluation. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_26

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  • DOI: https://doi.org/10.1007/978-3-031-16014-1_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16013-4

  • Online ISBN: 978-3-031-16014-1

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