Learning belief networks from data
Jie Cheng, David Bell, Weiru Liu · 1997
This paper presents an efficient algorithm for learning Bayesian belief networks from databases.The algorithm takes a database as input and constructs the belief network structure as output.The construction process is based on the computation of mutual information of attribute pairs.Given a data set that is large enough, this algorithm can generate a belief network very close to the underlying model, and at the same time, enjoys the time complexity of 0(N4) on conditional independence (CI) tests.When the data set has a normal DAG-Faithfil (see Section 3.2) probability distribution, the algorithm guarantees that the structure of a perfect map pearl, 19881 of the underlying dependency mode1 is generated.To evaluate this algorithm, we present the experimental results on three versions of the wellknown ALARM nehvork database, which has 37 attributes and 10,000 records.The results show that this algorithm is accurate and efficient.The proof of correctness and the analysis of computational complexity are also presented. 1 -*. 1 .