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Empirical study of individual feature evaluators and cutting criteria for feature selection in classification

Abstract : The use of feature selection can improve accuracy, efficiency, applicability and understandability of a learning process and its resulting model. For this reason, many methods of automatic feature selection have been developed. By using a modularization of feature selection process, this paper evaluates a wide spectrum of these methods. The methods considered are created by combination of different selection criteria and individual feature evaluation modules. These methods are commonly used because of their low running time. After carrying out a thorough empirical study the most interesting methods are identified and some recommendations about which feature selection method should be used under different conditions are provided.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00487214
Contributor : Magalie Prudon <>
Submitted on : Friday, May 28, 2010 - 12:06:16 PM
Last modification on : Thursday, September 24, 2020 - 5:22:03 PM

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Antonio Arauzo-Azofra, José-Luis Aznarte, José-M. Benitez. Empirical study of individual feature evaluators and cutting criteria for feature selection in classification. ISDA 2009 - 9th International Conference on Intelligent Systems Design and Application, Nov 2009, Pisa, Italy. pp.Pages 541-546 - ISBN: 978-076953872-3 - Article number 5364969, ⟨10.1109/ISDA.2009.175⟩. ⟨hal-00487214⟩

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