Journal of Intelligent Information Systems

, Volume 30, Issue 3, pp 273–292 | Cite as

Consistency measures for feature selection

  • Antonio Arauzo-Azofra
  • Jose Manuel Benitez
  • Juan Luis Castro


The use of feature selection can improve accuracy, efficiency, applicability and understandability of a learning process. For this reason, many methods of automatic feature selection have been developed. Some of these methods are based on the search of the features that allows the data set to be considered consistent. In a search problem we usually evaluate the search states, in the case of feature selection we measure the possible feature sets. This paper reviews the state of the art of consistency based feature selection methods, identifying the measures used for feature sets. An in-deep study of these measures is conducted, including the definition of a new measure necessary for completeness. After that, we perform an empirical evaluation of the measures comparing them with the highly reputed wrapper approach. Consistency measures achieve similar results to those of the wrapper approach with much better efficiency.


Feature selection Attribute evaluation Consistency Measures 


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Antonio Arauzo-Azofra
    • 1
  • Jose Manuel Benitez
    • 2
  • Juan Luis Castro
    • 2
  1. 1.Department of Rural EngineeringUniversity of CordobaCordobaSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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