Variable Selection for Naive Bayes Semisupervised Learning
Byoung-Jeong Choi, Kwangrae Kim, Kyu‐Dong Cho, Changyi Park, Ja‐Yong Koo · Communications in Statistics - Simulation and Computation · 2013
This article deals with a semisupervised learning based on naive Bayes assumption. A univariate Gaussian mixture density is used for continuous input variables whereas a histogram type density is adopted for discrete input variables. The EM algorithm is used for the computation of maximum likelihood estimators of parameters in the model when we fix the number of mixing components for each continuous input variable. We carry out a model selection for choosing a parsimonious model among various fitted models based on an information criterion. A common density method is proposed for the selection of significant input variables. Simulated and real datasets are used to illustrate the performance of the proposed method.