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An AdaBoost Method with K′K-Means Bayes Classifier for Imbalanced Data

Author

Listed:
  • Yanfeng Zhang

    (Department of Statistics, Beijing Jiaotong University, Beijing 100044, China)

  • Lichun Wang

    (Department of Statistics, Beijing Jiaotong University, Beijing 100044, China)

Abstract

This article proposes a new AdaBoost method with k ′ k-means Bayes classifier for imbalanced data. It reduces the imbalance degree of training data through the k ′ k-means Bayes method and then deals with the imbalanced classification problem using multiple iterations with weight control, achieving a good effect without losing any raw data information or needing to generate more relevant data manually. The effectiveness of the proposed method is verified by comparing it with other traditional methods based on numerical experiments. In the NSL-KDD data experiment, the F-score values of each minority class are also greater than the other methods.

Suggested Citation

  • Yanfeng Zhang & Lichun Wang, 2023. "An AdaBoost Method with K′K-Means Bayes Classifier for Imbalanced Data," Mathematics, MDPI, vol. 11(8), pages 1-11, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1878-:d:1124350
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    References listed on IDEAS

    as
    1. Gao, Lu & Lu, Pan & Ren, Yihao, 2021. "A deep learning approach for imbalanced crash data in predicting highway-rail grade crossings accidents," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Jian Huang & Junyi Chai & Stella Cho, 2020. "Deep learning in finance and banking: A literature review and classification," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-24, December.
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