Machine Learning
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Nearest Neighbors in Random Subspaces
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Identifying feature relevance using a random forest
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
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Many feature selection algorithms are limited in that they attempt to identify relevant feature subsets by examining the features individually. This paper introduces a technique for determining feature relevance using the average information gain achieved during the construction of decision tree ensembles. The technique introduces a node complexity measure and a statistical method for updating the feature sampling distribution based upon confidence intervals to control the rate of convergence. A feature selection threshold is also derived, using the expected performance of an irrelevant feature. Experiments demonstrate the potential of these methods and illustrate the need for both feature weighting and selection.