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Varying Naïve Bayes Models With Applications to Classification of Chinese Text Documents

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  • Guoyu Guan
  • Jianhua Guo
  • Hansheng Wang

Abstract

Document classification is an area of great importance for which many classification methods have been developed. However, most of these methods cannot generate time-dependent classification rules. Thus, they are not the best choices for problems with time-varying structures. To address this problem, we propose a varying naïve Bayes model, which is a natural extension of the naïve Bayes model that allows for time-dependent classification rule. The method of kernel smoothing is developed for parameter estimation and a BIC-type criterion is invented for feature selection. Asymptotic theory is developed and numerical studies are conducted. Finally, the proposed method is demonstrated on a real dataset, which was generated by the Mayor Public Hotline of Changchun, the capital city of Jilin Province in Northeast China.

Suggested Citation

  • Guoyu Guan & Jianhua Guo & Hansheng Wang, 2014. "Varying Naïve Bayes Models With Applications to Classification of Chinese Text Documents," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 445-456, July.
    • Handle: RePEc:taf:jnlbes:v:32:y:2014:i:3:p:445-456
    DOI: 10.1080/07350015.2014.903086
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    Cited by:

    1. Rafael Blanquero & Emilio Carrizosa & Pepa Ramírez-Cobo & M. Remedios Sillero-Denamiel, 2022. "Constrained Naïve Bayes with application to unbalanced data classification," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(4), pages 1403-1425, December.

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