Incremental Learning of Bayesian Networks from Concept-Drift Data

Bayesian network utilizes graphical model to describe dependencies among variables in probabilistic way, it is one of the most important model for uncertainty processing in Artificial Intelligence. Incremental learning of Bayesian networks has been received more attentions in recent years, in this paper a novel method is proposed to learn Bayesian network from incremental data. In this method, a novel incremental scoring function is designed to adaptively adjust the tendency of matching new and old data in the process of incremental learning. We propose an improved adaptive incremental structure learning algorithm for Bayesian network. Theoretical analysis and experimental results both demonstrate the proposed method outperforms other state-of-the-art methods.