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
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Identifying Markov Blankets with Decision Tree Induction
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A theoretical characterization of linear SVM-based feature selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Decomposition of structural learning about directed acyclic graphs
Artificial Intelligence
Towards scalable and data efficient learning of Markov boundaries
International Journal of Approximate Reasoning
Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures
ECML '07 Proceedings of the 18th European conference on Machine Learning
Using Markov Blankets for Causal Structure Learning
The Journal of Machine Learning Research
Scalable pseudo-likelihood estimation in hybrid random fields
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A comparison of novel and state-of-the-art polynomial Bayesian network learning algorithms
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Efficient Markov network structure discovery using independence tests
Journal of Artificial Intelligence Research
Decomposition of structural learning about directed acyclic graphs
Artificial Intelligence
Scalable statistical learning: a modular Bayesian/Markov network approach
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Learning Bayesian networks using evolutionary algorithm and a variant of MDL score
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
A partial correlation-based algorithm for causal structure discovery with continuous variables
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Local learning algorithm for markov blanket discovery
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
A decomposition algorithm for learning Bayesian network structures from data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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
Fast Markov blanket discovery algorithm via local learning within single pass
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Artificial Intelligence Review
BASSUM: A Bayesian semi-supervised method for classification feature selection
Pattern Recognition
Dynamic ordering-based search algorithm for markov blanket discovery
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
iMMPC: a local search approach for incremental Bayesian network structure learning
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Correlation-based and causal feature selection analysis for ensemble classifiers
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Scalable, efficient and correct learning of markov boundaries under the faithfulness assumption
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Learning Causal Relations in Multivariate Time Series Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Learning bayesian networks from Markov random fields: An efficient algorithm for linear models
ACM Transactions on Knowledge Discovery from Data (TKDD)
A Bayesian stochastic search method for discovering Markov boundaries
Knowledge-Based Systems
New skeleton-based approaches for Bayesian structure learning of Bayesian networks
Applied Soft Computing
Score-based methods for learning Markov boundaries by searching in constrained spaces
Data Mining and Knowledge Discovery
Estimating building simulation parameters via Bayesian structure learning
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Algorithms for discovery of multiple Markov boundaries
The Journal of Machine Learning Research
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Data Mining with Bayesian Network learning has two important characteristics: under conditions learned edges between variables correspond to casual influences, and second, for every variable T in the network a special subset (Markov Blanket) identifiable by the network is the minimal variable set required to predict T. However, all known algorithms learning a complete BN do not scale up beyond a few hundred variables. On the other hand, all known sound algorithms learning a local region of the network require an exponential number of training instances to the size of the learned region.The contribution of this paper is two-fold. We introduce a novel local algorithm that returns all variables with direct edges to and from a target variable T as well as a local algorithm that returns the Markov Blanket of T. Both algorithms (i) are sound, (ii) can be run efficiently in datasets with thousands of variables, and (iii) significantly outperform in terms of approximating the true neighborhood previous state-of-the-art algorithms using only a fraction of the training size required by the existing methods. A fundamental difference between our approach and existing ones is that the required sample depends on the generating graph connectivity and not the size of the local region; this yields up to exponential savings in sample relative to previously known algorithms. The results presented here are promising not only for discovery of local causal structure, and variable selection for classification, but also for the induction of complete BNs.