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A Membership Probability–Based Undersampling Algorithm for Imbalanced Data

Author

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  • Gilseung Ahn

    (Hanyang University)

  • You-Jin Park

    (National Taipei University of Technology)

  • Sun Hur

    (Hanyang University)

Abstract

Classifiers for a highly imbalanced dataset tend to bias in majority classes and, as a result, the minority class samples are usually misclassified as majority class. To overcome this, a proper undersampling technique that removes some majority samples can be an alternative. We propose an efficient and simple undersampling method for imbalanced datasets and show that the proposed method outperforms others with respect to four different performance measures by several illustrative experiments, especially for highly imbalanced datasets.

Suggested Citation

  • Gilseung Ahn & You-Jin Park & Sun Hur, 2021. "A Membership Probability–Based Undersampling Algorithm for Imbalanced Data," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 2-15, April.
    • Handle: RePEc:spr:jclass:v:38:y:2021:i:1:d:10.1007_s00357-019-09359-9
    DOI: 10.1007/s00357-019-09359-9
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    References listed on IDEAS

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    1. Douglas Steinley & Michael J. Brusco, 2007. "Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques," Journal of Classification, Springer;The Classification Society, vol. 24(1), pages 99-121, June.
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    2. Shiqi Wu & Hualong Yu & Yan Gu & Changbin Shao & Shang Gao, 2024. "SNN-PDM: An Improved Probability Density Machine Algorithm Based on Shared Nearest Neighbors Clustering Technique," Journal of Classification, Springer;The Classification Society, vol. 41(2), pages 289-312, July.

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