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

Guoyu Guan, Jianhua Guo and Hansheng Wang

Journal of Business & Economic Statistics, 2014, vol. 32, issue 3, 445-456

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.

Date: 2014
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:32:y:2014:i:3:p:445-456

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DOI: 10.1080/07350015.2014.903086

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