Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
ACM SIGKDD Explorations Newsletter
Optimal structure identification with greedy search
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
Towards scalable and data efficient learning of Markov boundaries
International Journal of Approximate Reasoning
Stability of feature selection algorithms: a study on high-dimensional spaces
Knowledge and Information Systems
Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Novel Scalable and Data Efficient Feature Subset Selection Algorithm
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Robust Feature Selection Using Ensemble Feature Selection Techniques
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Evaluating feature selection for SVMs in high dimensions
ECML'06 Proceedings of the 17th European conference on Machine Learning
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
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Our aim is to solve the feature subset selection problem with thousands of variables using an incremental procedure. The procedure combines incrementally the outputs of non-scalable search-and-score Bayesian network structure learning methods that are run on much smaller sets of variables. We assess the scalability, the performance and the stability of the procedure through several experiments on synthetic and real databases scaling up to 139 351 variables. Our method is shown to be efficient in terms of both running time and accuracy.