Advances in neural information processing systems 2
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Introduction to algorithms
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Weighted mutual information for feature selection
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
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Within the taxonomy of feature extraction methods, recently the Wrapper approaches lost some popularity due to the associated computational burden, compared to Embedded or Filter methods. The dominating factor in terms of computational costs is the number of adaption cycles used to train the black box classifier or function approximator, e.g. a Multi Layer Perceptron. To keep a wrapper approach feasible, the number of adaption cycles has to be minimized, without increasing the risk of missing important feature subset combinations. We propose a search strategy, that exploits the interesting properties of Chow-Liu trees to reduce the number of considered subsets significantly. Our approach restricts the candidate set of possible new features in a forward selection step to children from certain tree nodes. We compare our algorithm with some basic and well known approaches for feature subset selection. The results obtained demonstrate the efficiency and effectiveness of our method.