Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Applying Two-Level Simulated Annealing on Bayesian Structure Learning to Infer Genetic Networks
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
A chain-model genetic algorithm for Bayesian network structure learning
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Learning Bayesian networks from data is an NP-hard problem with important practical applications. Metaheuristic search on the space of node orderings combined with deterministic construction and scoring of a network is a well-established approach. The comparative performance of different search and score algorithms is highly problemdependent and so it is of interest to analyze, for benchmark problems with known structures, the relationship between problem features and algorithm performance. In this paper, we investigate four combinations of search (Genetic Algorithms or Ant Colony Optimization) with scoring (K2 or Chain). We relate node juxtaposition distributions over a number of runs to the known problem structure, the algorithm performance and the detailed algorithmic processes. We observe that, for different reasons, ACO and Chain both focus the search on a narrower range of orderings. This works well when the underlying structure is compatible but poorly otherwise. We conclude by suggesting future directions for research.