Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An algorithm for deciding if a set of observed independencies has a causal explanation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Permutation Genetic Algorithm For Variable Ordering In Learning Bayesian Networks From Data
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Applying Two-Level Simulated Annealing on Bayesian Structure Learning to Infer Genetic Networks
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Learning Bayesian Networks
Towards efficient variables ordering for Bayesian networks classifier
Data & Knowledge Engineering
A chain-model genetic algorithm for Bayesian network structure learning
Proceedings of the 9th annual conference on Genetic and evolutionary computation
IEEE Transactions on Knowledge and Data Engineering
A simple graphical approach for understanding probabilistic inference in Bayesian networks
Information Sciences: an International Journal
Learning Non-Stationary Dynamic Bayesian Networks
The Journal of Machine Learning Research
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A construction of Bayesian networks from databases based on an MDL principle
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
IEEE Transactions on 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
Comparison of score metrics for Bayesian network learning
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
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In Bayesian networks, the K2 algorithm is one of the most effective structure-learning methods. However, because the performance of the K2 algorithm depends on node ordering, more effective node ordering inference methods are needed. In this paper, we therefore introduce a new node ordering algorithm based on a novel scoring function. Because a child has a better conditional frequency or probability under a correct parent than an incorrect one, we have designed a novel scoring function to evaluate this conditional frequency. Given two variables, our scoring function infers which is the better parent variable. Consequently, the proposed method infers candidate parents by considering all pairs of variables; it then uses these parents as input for the K2 algorithm. Experimental results indicate that our proposed method outperforms previous methods.