Extended Tree Augmented Naive Bayesian Classifier
Li Xu · 2006
Tree Augmented Naive Bayesian Classifier (TAN) often outperforms Naive Bayesian, yet at the same time maintains the computational simplicity and robustness that characterize Naive Bayesian. But TAN often requires a prior discretization of continuous variables. It is important to investigate mixed-mode data, in order to represent data distributions well and avoid the problem of information loss. In this paper, the maximum likelihood function of hybrid data is deduced, and a new classifier called Extended Tree Augmented Naive Bayesian Classifier (ETAN) is put forward. The proposed classifier breaks through the restriction that continuous variables must be discretized, and it can deal with hybrid variables in the framework of TAN. Experiments show that this classifier has a good accuracy of classification.