Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
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
Improved learning of Bayesian networks
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Information-theoretic inference of large transcriptional regulatory networks
EURASIP Journal on Bioinformatics and Systems Biology
A hybrid anytime algorithm for the construction of causal models from sparse data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning Bayesian networks from incomplete data with stochastic search algorithms
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
Probabilistic Information Structure of Human Walking
Journal of Medical Systems
Artificial Intelligence in Medicine
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Bayesian networks (BNs) represent one of the most successful tools for medical diagnosis, selection of the optimal treatment, and prediction of the treatment outcome. In this paper, we present an algorithm for BN structure learning, which is a variation of the standard search-and-score approach. The proposed algorithm overcomes the creation of redundant network structures that may include nonsignificant connections between variables. In particular, the algorithm finds what relationships between the variables must be prevented, by exploiting the binarization of a square matrix containing the mutual information (MI) among all pairs of variables. Two different binarization methods are implemented. The first one is based on the maximum relevance minimum redundancy selection strategy. The second one uses a threshold. The MI binary matrix is exploited as a preconditioning step for the subsequent greedy search procedure that optimizes the network score, reducing the number of possible search paths in the greedy search. Our algorithm has been tested on two different medical datasets and compared against the standard search-and-score algorithm as implemented in the DEAL package.