An incremental structure learning approach for Bayesian Network
Shuo Li, Jun Zhang, Boliang Sun, Jun Wei Lei · 2014
Structure learning of Bayesian Network (BN) is one of important topics in machine learning and widely applied in expert system. The traditional algorithms for structure learning are usually focused on the batch data in nature. It is difficult to learn the structure quickly from the huge amounts of data. But in many practical applications, the structure of BN should be learned by using time-series data that are available to us. To achieve this goal, we propose an incremental structure learning approach for BN. Firstly, we proposed the framework of incremental structure learning and a new evaluation criterion “ABIC” (Adopt Bayesian Information Criterion) based on the BIC. Then, three phase algorithm is used to learn the structure. Numerical experiments on two standard networks show that our proposed algorithm can greatly improve the accuracy of the structure and the total of learning time is greatly reduced.