Learning Bayesian Belief Network Classifiers: Algorithms and System [chapter]

Jie Cheng, Russell Greiner
2001 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 show
more » ... the proposed BN and Bayes multi-net classifiers are competitive with (or superior to) the best known classifiers, based on both BN and other formalisms; and that the computational time for learning and using these classifiers is relatively small. These results argue that BN based classifiers deserve more attention in the data mining community.
doi:10.1007/3-540-45153-6_14 fatcat:rvjewedkhbbqtinjtpaoqthga4