Bayesian network classifiers based on Gaussian kernel density. (1st June 2016) - Wang, Shuang-cheng; Gao, Rui; Wang, Li-min
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. Abstract: For learning a Bayesian network classifier, continuous attributes usually need to be discretized. But the discretization of continuous attributes may bring information missing, noise and less sensitivity to the changing of the attributes towards class variables. In this paper, we use the Gaussian kernel function with smoothing parameter to estimate the density of attributes. Bayesian network classifier with continuous attributes is established by the dependency extension of Naive Bayes classifiers. We also analyze the information provided to a class for each attributes as a basis for the dependency extension of Naive Bayes classifiers. Experimental studies on UCI data sets show that Bayesian network classifiers using Gaussian kernel function provide good classification accuracy comparing to other approaches when dealing with continuous attributes.