IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Two Evolutionary Methods for Learning Bayesian Network Structures
Computational Intelligence and Security
Learning Transcriptional Regulatory Networks with Evolutionary Algorithms Enhanced with Niching
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
A heuristic method for learning Bayesian networks using discrete particle swarm optimization
Knowledge and Information Systems
Data analysis with bayesian networks: a bootstrap approach
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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In machine learning, graphical models like Bayesian networks are one of important visualization tools that can be learned from data to represent pictorially a complex system. In order to compare two complex systems (or one complex system functioning in two different contexts), one usually compares directly their representative graphs. However, with small sample size data, it is hard to learn the graph that represents precisely the system. That's why ensemble methods (e.g. Bootstrapping, evolutionary algorithm, etc...) are proposed to learn from data of each system a set of graphs that represents more precisely this system. Then, for comparing two systems, one needs a mechanism to compare two sets of graphs. We propose in this work an approach based on multiple hypothesis testing and quasi essential graph (QEG) to compare two sets of Bayesian networks.