Bayesian network classifiers based on Gaussian kernel density |
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| Affiliation: | 1. Lixin Accounting Research Institute, Shanghai Lixin University of Commerce, Shanghai 201620, China;2. School of Mathematics and Information, Shanghai Lixin University of Commerce, Shanghai 201620, China;3. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China;4. College of Computer Science and Technology, Jilin University, Changchun 130012, China;1. Department of Industrial and Systems Engineering, Ohio University, Athens, OH 45701, USA;2. Management Department, College of Business, Ohio University, Athens, OH 45701, USA;3. Department of Mechanical and Industrial Engineering, Concordia University, Montreal, Quebec H3G 1M8, Canada;1. Department of Economics and Business Economics, Aarhus University, Denmark;2. Department of Civil Engineering, The University of Hong Kong, Hong Kong, China;1. Department of Electrical Engineering, University of Huelva, Carretera Palos-Huelva, s/n., 21071 Palos de la Frontera, Huelva, Spain;2. Research Group in Electrical Technologies for Sustainable and Renewable Energy (PAIDI-TEP-023), Department of Electrical Engineering, EPS Algeciras, University of Cádiz, Avda. Ramón Puyol, s/n., 11202 Algeciras, Cádiz, Spain;3. Research Group in Research and Electrical Technology (PAIDI-TEP-152), Department of Electrical Engineering, EPS Linares, University of Jaén, C/ Alfonso X, nº 28., 23700 Linares, Jaén, Spain |
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| 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. |
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