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Alleviating conditional independence assumption of naive Bayes

Author

Listed:
  • Xu-Qing Liu

    (Huaiyin Institute of Technology)

  • Xiao-Cai Wang

    (Huaiyin Institute of Technology)

  • Li Tao

    (Huaiyin Institute of Technology)

  • Feng-Xian An

    (Huaiyin Institute of Technology)

  • Gui-Ren Jiang

    (Huaiyin Institute of Technology)

Abstract

In this paper, we consider the problem of how to alleviate the conditional independence assumption of naive Bayes. We try to find an equivalent set of variables for the attributes of the class such that these variables are nearly conditionally independent. For the case that all attributes are continuous variables, we put forward the theory of class-weighting supervised principal component analysis (CWSPCA) to improve naive Bayes. For the categorical case, we construct the equivalent variables by rearranging the values of the attributes, and propose the decremental association rearrangement (DAR) algorithm and its multiple version (MDAR). Finally, we make a benchmarking study to show the performance of our methods. The experimental results reveal that naive Bayes can be greatly improved by means of properly transforming the original attributes.

Suggested Citation

  • Xu-Qing Liu & Xiao-Cai Wang & Li Tao & Feng-Xian An & Gui-Ren Jiang, 2024. "Alleviating conditional independence assumption of naive Bayes," Statistical Papers, Springer, vol. 65(5), pages 2835-2863, July.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:5:d:10.1007_s00362-023-01474-5
    DOI: 10.1007/s00362-023-01474-5
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    References listed on IDEAS

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    1. Bair, Eric & Hastie, Trevor & Paul, Debashis & Tibshirani, Robert, 2006. "Prediction by Supervised Principal Components," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 119-137, March.
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