Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Speculative Markov Blanket Discovery for Optimal Feature Selection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning Bayesian Networks
Consistent Feature Selection for Pattern Recognition in Polynomial Time
The Journal of Machine Learning Research
Towards scalable and data efficient learning of Markov boundaries
International Journal of Approximate Reasoning
Bounding the false discovery rate in local Bayesian network learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Tradeoff analysis of different Markov blanket local learning approaches
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
An efficient and scalable algorithm for local Bayesian network structure discovery
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks
Artificial Intelligence in Medicine
Analysis of Markov Boundary Induction in Bayesian Networks: A New View From Matroid Theory
Fundamenta Informaticae
An experimental comparison of hybrid algorithms for bayesian network structure learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours
Computers in Biology and Medicine
Artificial Intelligence in Medicine
Supervised feature subset selection with ordinal optimization
Knowledge-Based Systems
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We aim to identify the minimal subset of random variables that is relevant for probabilistic classification in data sets with many variables but few instances. A principled solution to this problem is to determine the Markov boundary of the class variable. In this paper, we propose a novel constraint-based Markov boundary discovery algorithm called MBOR with the objective of improving accuracy while still remaining scalable to very high dimensional data sets and theoretically correct under the so-called faithfulness condition. We report extensive empirical experiments on synthetic data sets scaling up to tens of thousand variables.