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Conference papers
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.
https://hal-mines-paristech.archives-ouvertes.fr/hal-00487214
Contributor : Magalie Prudon Connect in order to contact the contributor
Submitted on : Friday, May 28, 2010 - 12:06:16 PM
Last modification on : Wednesday, November 17, 2021 - 12:30:15 PM
Contributor : Magalie Prudon Connect in order to contact the contributor
Submitted on : Friday, May 28, 2010 - 12:06:16 PM
Last modification on : Wednesday, November 17, 2021 - 12:30:15 PM