An efficient parameter estimation method for generalized Dirichlet priors in naïve Bayesian classifiers with multinomial models

作者：

Highlights：

• An efficient method for constructing noninformative GD priors is proposed.

• The best strategy is to choose the largest candidate value for a parameter.

• A small default value is necessary for the distinct words with small frequencies.

• This method significantly improves the performance of naïve Bayesian classifiers.

摘要

•An efficient method for constructing noninformative GD priors is proposed.•The best strategy is to choose the largest candidate value for a parameter.•A small default value is necessary for the distinct words with small frequencies.•This method significantly improves the performance of naïve Bayesian classifiers.

论文关键词：Covariance matrix,Document classification,Generalized Dirichlet distribution,Multinomial model,Naïve Bayesian classifier

论文评审过程：Received 13 May 2015, Revised 4 March 2016, Accepted 29 April 2016, Available online 19 May 2016, Version of Record 31 May 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.04.019