Empirical study of feature selection methods based on individual feature evaluation for classification problems

Authors:
Antonio Arauzo-Azofra;José Luis Aznarte;José M. Benítez
Affiliations:
Area of Project Engineering, University of Córdoba, Campus de Rabanales, 14071 Córdoba, Spain;Centre for Energy and Processes, Ecole des Mines de Paris, Sophia Antipolis, France;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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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.