Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on learning with probabilistic representations
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speculative Markov Blanket Discovery for Optimal Feature Selection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning Bayesian Networks
The Journal of Machine Learning Research
Towards scalable and data efficient learning of Markov boundaries
International Journal of Approximate Reasoning
Bayesian network learning algorithms using structural restrictions
International Journal of Approximate Reasoning
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
Using Markov Blankets for Causal Structure Learning
The Journal of Machine Learning Research
A Fast Hill-Climbing Algorithm for Bayesian Networks Structure Learning
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
S-IAMB Algorithm for Markov Blanket Discovery
APCIP '09 Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02
Journal of Artificial Intelligence Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
On sensitivity of case-based reasoning to optimal feature subsets in business failure prediction
Expert Systems with Applications: An International Journal
Local learning algorithm for markov blanket discovery
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
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
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Models and selection criteria for regression and classification
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Theory refinement on Bayesian networks
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Feature subset selection by genetic algorithms and estimation of distribution algorithms
Artificial Intelligence in Medicine
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Within probabilistic classification problems, learning the Markov boundary of the class variable consists in the optimal approach for feature subset selection. In this paper we propose two algorithms that learn the Markov boundary of a selected variable. These algorithms are based on the score+search paradigm for learning Bayesian networks. Both algorithms use standard scoring functions but they perform the search in constrained spaces of class-focused directed acyclic graphs, going through the space by means of operators adapted for the problem. The algorithms have been validated experimentally by using a wide spectrum of databases, and their results show a performance competitive with the state-of-the-art.