ACM SIGKDD Explorations Newsletter
An introduction to variable and feature selection
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
Nasopharyngeal Carcinoma Data Analysis with a Novel Bayesian Network Skeleton Learning Algorithm
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Robust independence testing for constraint-based learning of causal structure
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Learning bayesian networks in semi-deterministic systems
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Exploiting Data Missingness in Bayesian Network Modeling
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Selecting and Weighting Data for Building Consensus Gene Regulatory Networks
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
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
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
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
Score-based methods for learning Markov boundaries by searching in constrained spaces
Data Mining and Knowledge Discovery
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In this paper, we aim to identify the minimal subset of discrete 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 boundaryof the class variable. Also, we present a novel scalable, data efficient and correct Markov boundary learning algorithm under the so-called faithfulnesscondition. We report extensive empiric experiments on synthetic and real data sets scaling up to 139,351 variables.