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

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.

KEY WORDS:

• BIC
• Chinese document classification
• Screening consistency
• Time-dependent classification rule

ACKNOWLEDGMENTS

Guan and Guo’s research was supported in part by the National Natural Science Foundation of China (No. 11025102), Natural Science Foundation of Jilin Province (No. 20100401), Program for Changjiang Scholars and Innovative Research Team in University. Wang’s research was supported in part by National Natural Science Foundation of China (No. 11131002 and No. 11271032), Fox Ying Tong Education Foundation, the Business Intelligence Research Center at Peking University, and the Center for Statistical Science at Peking University.