Varying Naive Bayes Models with Applications to Classification of Chinese Text Documents
32 Pages Posted: 9 Feb 2015
Date Written: February 9, 2015
Document classification is an area of great importance for which many classification methods have been well 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 naive Bayes model, which is a natural extension of the naive 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.
Keywords: BIC; Chinese Document Classification; Screening Consistency; Time-dependent Classification Rule; Varying Naive Bayes
JEL Classification: C35
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