Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Instance-Based Learning Algorithms
Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning Bayesian Networks
Data Mining
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In this paper I propose a novel feature selection technique based on Bayesian networks. The main idea is to exploit the conditional independencies entailed by Bayesian networks in order to discard features that are not directly relevant for classification tasks. An algorithm for learning Bayesian networks and its use in feature selection are illustrated. The advantages of this algorithm with respect to other ones are then discussed. Finally, experimental results are offered which confirm the reliability of the algorithm.