Bayesian Network Refinement Via Machine Learning Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
A Doctrine of Cognitive Informatics (CI)
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (I)
Contemporary cybernetics and its facets of cognitive informatics and computational intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Sequential update of Bayesian network structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Editorial Recent Advances in Cognitive Informatics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this paper, a group of hybrid incremental learning algorithms for Bayesian network structures are proposed. The central idea of these hybrid algorithms is to use the polynomial-time constraint-based technique to build a candidate parent set for each domain variable, followed by the hill climbing search procedure to refine the current network structure under the guidance of the candidate parent sets. Experimental results show that, the authors' hybrid incremental algorithms offer considerable computational complexity savings while obtaining better model accuracy compared to the existing incremental algorithms. One of their hybrid algorithms is also used to model financial data generated from American stock exchange markets. It finds out the predictors of the stock return among hundreds of financial variables and, at the same time, the authors' algorithm also can recover the movement trend of the stock return.