Bayesian network classifiers based on Gaussian kernel density
作者：
Highlights：
• We construct ENBC by imposing dependency extension on NBC with continuous attributes.
• We combine smoothing parameter adjustment and the structure learning.
• We control and optimize the fitting degree between classifier and data.
• We present that the attributes of ENBC provide three types of information for class.
• The other two information improve the classification accuracy effectively.
摘要
•We construct ENBC by imposing dependency extension on NBC with continuous attributes.•We combine smoothing parameter adjustment and the structure learning.•We control and optimize the fitting degree between classifier and data.•We present that the attributes of ENBC provide three types of information for class.•The other two information improve the classification accuracy effectively.
论文关键词：Bayesian network classifiers,Continuous attributes,Gaussian kernel function,Smoothing parameters,Classification accuracy
论文评审过程：Received 28 July 2015, Revised 1 December 2015, Accepted 21 December 2015, Available online 7 January 2016, Version of Record 23 January 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.12.031
