Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data
Pattern Recognition Letters - special issue on pattern recognition in practice V
Construction of Large-Scale Bayesian Networks by Local to Global Search
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
an entropy-driven system for construction of probabilistic expert systems from databases
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Learning Bayesian Networks
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
AIS-Based Bootstrapping of Bayesian Networks for Identifying Protein Energy Route
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
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We propose a recursive clustering and order restriction (R-CORE) method for learning large-scale Bayesian networks. The proposed method considers a reduced search space for directed acyclic graph (DAG) structures in scoring-based Bayesian network learning. The candidate DAG structures are restricted by clustering variables and determining the intercluster directionality. The proposed method considers cycles on only cmax(n) variables rather than on all n variables for DAG structures. The R-CORE method could be a useful tool in very large problems where only a very small amount of training data is available.