Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on learning with probabilistic representations
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
ACM SIGKDD Explorations Newsletter
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Discriminative versus generative parameter and structure learning of Bayesian network classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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 network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
On supervised selection of Bayesian networks
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
MIDAS - an influence diagram for management of mildew in winter wheat
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Robust independence testing for constraint-based learning of causal structure
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Comparison of score metrics for Bayesian network learning
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Incorporating expert knowledge when learning Bayesian network structure: A medical case study
Artificial Intelligence in Medicine
Adaptive thresholding in structure learning of a Bayesian network
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 0.00 |
We propose the recursive autonomy identification (RAI) algorithm for constraint-based (CB) Bayesian network structure learning. The RAI algorithm learns the structure by sequential application of conditional independence (CI) tests, edge direction and structure decomposition into autonomous sub-structures. The sequence of operations is performed recursively for each autonomous sub-structure while simultaneously increasing the order of the CI test. While other CB algorithms d-separate structures and then direct the resulted undirected graph, the RAI algorithm combines the two processes from the outset and along the procedure. By this means and due to structure decomposition, learning a structure using RAI requires a smaller number of CI tests of high orders. This reduces the complexity and run-time of the algorithm and increases the accuracy by diminishing the curse-of-dimensionality. When the RAI algorithm learned structures from databases representing synthetic problems, known networks and natural problems, it demonstrated superiority with respect to computational complexity, run-time, structural correctness and classification accuracy over the PC, Three Phase Dependency Analysis, Optimal Reinsertion, greedy search, Greedy Equivalence Search, Sparse Candidate, and Max-Min Hill-Climbing algorithms.