Learning optimal Bayesian networks using A* search

Authors:
Changhe Yuan;Brandon Malone;Xiaojian Wu
Affiliations:
Department of Computer Science and Engineering, Mississippi State University;Department of Computer Science and Engineering, Mississippi State University;Department of Computer Science, University of Massachusetts, Amherst
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Year:
2011

Citing 7
Cited 3

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Learning equivalence classes of bayesian-network structures

The Journal of Machine Learning Research
Exact Bayesian Structure Discovery in Bayesian Networks

The Journal of Machine Learning Research
Structure learning of Bayesian networks using constraints

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Cached sufficient statistics for efficient machine learning with large datasets

Journal of Artificial Intelligence Research
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A branch-and-bound algorithm for MDL learning Bayesian networks

UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence

Finding optimal Bayesian networks using precedence constraints

The Journal of Machine Learning Research
Parameterized complexity results for exact bayesian network structure learning

Journal of Artificial Intelligence Research
Learning optimal bayesian networks: a shortest path perspective

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper formulates learning optimal Bayesian network as a shortest path finding problem. An A* search algorithm is introduced to solve the problem. With the guidance of a consistent heuristic, the algorithm learns an optimal Bayesian network by only searching the most promising parts of the solution space. Empirical results show that the A* search algorithm significantly improves the time and space efficiency of existing methods on a set of benchmark datasets.