A hybrid approach for learning Markov equivalence classes of Bayesian network

Authors:
Haiyang Jia;Dayou Liu;Juan Chen;Xin Liu
Affiliations:
College of Computer Science and Technology, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China;College of Computer Science and Technology, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China;College of Computer Science and Technology, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China;College of Computer Science and Technology, Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
Venue:
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Year:
2007

Citing 6
Cited 1

An algorithm for deciding if a set of observed independencies has a causal explanation

UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Generating all the acyclic orientations of an undirected graph

Information Processing Letters
Learning Bayesian networks from data: an information-theory based approach

Artificial Intelligence
Equivalence and synthesis of causal models

UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Learning equivalence classes of bayesian-network structures

The Journal of Machine Learning Research
Large-Sample Learning of Bayesian Networks is NP-Hard

The Journal of Machine Learning Research

Review: learning bayesian networks: Approaches and issues

The Knowledge Engineering Review

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bayesian Networks is a popular tool for representing uncertainty knowledge in artificial intelligence fields. Learning BNs from data is helpful to understand the casual relation between the variable. But Learning BNs is a NP hard problem. This paper presents a novel hybrid algorithm for learning Markov Equivalence Classes, which combining dependency analysis and search-scoring approach together. The algorithm uses the constraint to perform a mapping from skeleton to MEC. Experiments show that the search space was constrained efficiently and the computational performance was improved.