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Lecture Notes in Computer Science
This paper investigates the methods for learning predictive classifiers based on Bayesian belief networks (BN) -primarily unrestricted Bayesian networks and Bayesian multi-nets. We present our algorithms for learning these classifiers, and discuss how these methods address the overfitting problem and provide a natural method for feature subset selection. Using a set of standard classification problems, we empirically evaluate the performance of various BN-based classifiers. The results showdoi:10.1007/3-540-45153-6_14 fatcat:rvjewedkhbbqtinjtpaoqthga4