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Evidence-based adaptive oversampling algorithm for imbalanced classification

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Abstract

Classification task is complicated by several facts including skewed class proportion and unclear decision regions due to noise, class overlap, small disjunct, caused by large within-class variation. These issues make data classification difficult, reducing overall performance, and challenging to draw meaningful insights. In this research, the evidence-based adaptive oversampling algorithm (EVA-oversampling) based on Dempster–Shafer theory of evidence is developed for imbalance classification. This technique involves assigning probability regarding class belonging for each instance to represent uncertainty that each data point may hold. Synthetic data points are generated to make up for the under-representation of minority instances on the region with high confidence, thereby strengthening the minority class region. The experiments revealed that the proposed method worked effectively even in situations where imbalanced counts and data complexity would normally pose significant obstacles. This approach performs better than SMOTE, Borderline-SMOTE, ADASYN, MWMOTE, KMeansSMOTE, LoRAS, and SyMProD algorithms in terms of \(F_1\)-measure and G-mean for highly imbalanced data while maintaining the overall performance.

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Data Availability

The datasets used in this study are available online at https://archive.ics.uci.edu/ml/index.php.

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Acknowledgements

Not applicable.

Funding

This work was supported by the Ministry of Science and Technology, Taiwan under Grant No. MOST 110-2221-E-155-060-MY2.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan City, Taiwan R.O.C.

    Chen-ju Lin & Florence Leony

  2. Department of Industrial Engineering, Universitas Kristen Maranatha, Bandung, Indonesia

    Florence Leony

Authors
  1. Chen-ju Lin
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  2. Florence Leony
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Contributions

Chen-ju Lin was involved in the conceptualization, methodology, reviewing, supervision, funding acquisition. Florence Leony contributed to the methodology, data curation, formal analysis, visualization, investigation, writing, editing. All authors reviewed the manuscript.

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Correspondence to Florence Leony.

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Lin, Cj., Leony, F. Evidence-based adaptive oversampling algorithm for imbalanced classification. Knowl Inf Syst 66, 2209–2233 (2024). https://doi.org/10.1007/s10115-023-01985-5

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