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ICANN
2007
Springer

An Efficient Search Strategy for Feature Selection Using Chow-Liu Trees

9 years 9 months ago
An Efficient Search Strategy for Feature Selection Using Chow-Liu Trees
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.
Erik Schaffernicht, Volker Stephan, Horst-Michael
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2007
Where ICANN
Authors Erik Schaffernicht, Volker Stephan, Horst-Michael Groß
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