Robust Structure Learning of Bayesian Network by Identifying Significant Dependencies

Bayesian networks have long been a popular medium for graphically representing the probabilistic dependencies which exist in a domain. State-of-the-art tree-augmented naive Bayes (TAN) builds maximum weighted spanning tree to represent 1-dependence relationships between attributes. In this paper, we propose to optimize the structure of TAN applying heuristic search to sort attribute and filtering technique to remove weak conditional dependencies. Extensive experimental results on 35 data sets from University of California at Irvine (UCI) machine learning repository reveal that the proposed algorithm achieves competitive generalization performance and even outperforms higher-dependence BNCs like <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-dependence Bayesian network while retaining excellent strucutre complexity.

Abstract licence: CC BY-NC-ND

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.