Varying Naïve Bayes Models With Applications to Classification of Chinese Text Documents
针对传统分类方法无法处理时变结构的问题，提出变朴素贝叶斯模型，允许分类规则随时间变化，并通过核平滑估计参数和BIC准则选择特征，在长春市长公开电话数据集上验证了有效性。
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.