Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
Machine Learning - Special issue on learning with probabilistic representations
ACM SIGKDD Explorations Newsletter
Time and sample efficient discovery of Markov blankets and direct causal relations
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Bayesian Networks
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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
A hybrid Bayesian network learning method for constructing gene networks
Computational Biology and Chemistry
Adapting Bayes network structures to non-stationary domains
International Journal of Approximate Reasoning
Using Markov Blankets for Causal Structure Learning
The Journal of Machine Learning Research
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Incremental Bayesian Network Learning for Scalable Feature Selection
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
The Journal of Machine Learning Research
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We propose an algorithm for learning the Markov boundary of a random variable from data without having to learn a complete Bayesian network. The algorithm is correct under the faithfulness assumption, scalable and data efficient. The last two properties are important because we aim to apply the algorithm to identify the minimal set of random variables that is relevant for probabilistic classification in databases with many random variables but few instances. We report experiments with synthetic and real databases with 37, 441 and 139352 random variables showing that the algorithm performs satisfactorily.