A New Variable Ordering Method for the K2 Algorithm

Abstract

K2 is an algorithm used for learning the structure of a Bayesian networks (BN). The performance of the K2 algorithm depends on the order of the variables. If the given ordering is not sufficient, the score of the network structure is found to be low. We proposed a new variable ordering method in order to find the hierarchy of the variables. The proposed method was compared with other methods by using synthetic and real-world data sets. Experimental results show that the proposed method is efficient in terms of both time and score.