A two phase approach to Bayesian network model selection and comparison between the MDL and DGM scoring heuristics
Michael J. Kane, Ferat Sahin, Andreas E. Savakis · 2004
This paper presents an efficient algorithm for learning a Bayesian belief network (BBN) structure from a database, as well as providing a comparison between two BBN structure fitness functions. A Bayesian belief network is a directed acyclic graph representing conditional expectations. In this paper, we propose a two-phase algorithm. The first phase uses asymptotically correct structure learning for efficient search space exploration, while the second phase uses greedy model selection for accurate search space exploration. The minimum description length (MDL) structure fitness function is also compared with the database given model probability (DGM) fitness function in the second phase. The model selection algorithms are applied to the ALARM network to provide a comparison for the accuracy of the techniques.