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An Introduction to Variable and Feature Selection.

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Elisseeff,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Guyon, I., & Elisseeff, A. (2003). An Introduction to Variable and Feature Selection. The Journal of Machine Learning Research, 3, 1157-1182.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-DD5E-E
Abstract
Variable and feature selection have become the focus of much research in areas of application for which data sets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry.The objective of variable selection is three-fold: improving the prediction performance of the pre-dictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.