Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
A Hybrid Data Mining Approach To Discover Bayesian Networks Using Evolutionary Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Probalistic Network Construction Using the Minimum Description Length Principle
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Building a GA from design principles for learning Bayesian networks
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Parameterising bayesian networks
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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
Evolved bayesian networks as a versatile alternative to partin tables for prostate cancer management
Proceedings of the 10th annual conference on Genetic and evolutionary computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Artificial Intelligence Review
Privacy-preserving approach to bayesian network structure learning from distributed data
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Artificial Intelligence in Medicine
Competing mutating agents for bayesian network structure learning
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
An efficient node ordering method using the conditional frequency for the K2 algorithm
Pattern Recognition Letters
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Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]. The super exponential growth of the number of possible networks given the number of factors in the studied problem domain has meant that more often, approximate and heuristic rather than exact methods are used. In this paper, a novel genetic algorithm approach for reducing the complexity of Bayesian network structure discovery is presented. We propose a method that uses chain structures as a model for Bayesian networks that can be constructed from given node orderings. The chain model is used to evolve a small number of orderings which are then injected into a greedy search phase which searches for an optimal structure. We present a series of experiments that show a significant reduction can be made in computational cost although with some penalty in success rate.