PAM: An efficient hybrid dimension reduction algorithm for high-dimensional bayesian network

Abstract

In recent years, machine learning has been gradually widely applied to the big data in medical field, such as prediction and prevention of disorders. Bayesian Network has been playing an important role in machine learning and has been widely applied to the medical diagnosis field for its advantages in reasoning under uncertainty. But as the rise of the number of interest variables, the Bayesian Network structure search space is growing super-exponentially. Aiming at improving the efficiency of finding the optimize structure from the large search space of high-dimensional network, in this paper we propose a method, PAM, which is applied to Bayesian Network learning to constrain the search space of high-dimensional network. Several Experiments are performed in order to confirm our hypothesis.