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Variable Selection for Naive Bayes Semisupervised Learning

Authors
Choi, Byoung-JeongKim, Kwang-RaeCho, Kyu-DongPark, ChangyiKoo, Ja-Yong
Issue Date
26-11월-2014
Publisher
TAYLOR & FRANCIS INC
Keywords
BIC; Common density; Density estimation; EM algorithm; Model selection; Naive Bayes; Semisupervised learning; Variable selection
Citation
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.43, no.10, pp.2702 - 2713
Indexed
SCIE
SCOPUS
Journal Title
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume
43
Number
10
Start Page
2702
End Page
2713
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/96732
DOI
10.1080/03610918.2012.762391
ISSN
0361-0918
Abstract
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.
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