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Naïve Bayes classifier based on reliability measurement for datasets with noisy labels

Author

Listed:
  • Yingqiu Zhu

    (University of International Business and Economics)

  • Yinzhi Wang

    (University of International Business and Economics)

  • Lei Qin

    (University of International Business and Economics
    Wuhan University)

  • Bo Zhang

    (Renmin University of China)

  • Ben-Chang Shia

    (Fu Jen Catholic University
    Fu Jen Catholic University)

  • MingChih Chen

    (Fu Jen Catholic University
    Fu Jen Catholic University)

Abstract

Incorrect labeling is a common issue that often occurs in machine learning applications. If datasets contain noisy labels and these errors are not corrected, the performance of the trained classifiers is affected significantly. In order to address this issue, we present a reliability measurement for labels, which is generated based on crowdsourcing. We adopt this reliability measurement to improve the Naïve Bayes classifier, resulting in a reliability measurement-based approach. Additionally, we explain the generating mechanism of incorrect labels and employ an iterative EM algorithm to optimize the corresponding log-likelihood function. This enables us to estimate the necessary parameters for the reliability measurement-based Naïve Bayes classifier. The simulation and experimental results demonstrate that the proposed method significantly improves the performance of Naïve Bayes classifier for datasets containing noisy labels.

Suggested Citation

  • Yingqiu Zhu & Yinzhi Wang & Lei Qin & Bo Zhang & Ben-Chang Shia & MingChih Chen, 2025. "Naïve Bayes classifier based on reliability measurement for datasets with noisy labels," Annals of Operations Research, Springer, vol. 349(1), pages 259-286, June.
    • Handle: RePEc:spr:annopr:v:349:y:2025:i:1:d:10.1007_s10479-023-05671-1
    DOI: 10.1007/s10479-023-05671-1
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