KEEL: a software tool to assess evolutionary algorithms for data mining problems

  • Authors:

  • J. Alcalá-Fdez;L. Sánchez;S. García;M. J. del Jesus;S. Ventura;J. M. Garrell;J. Otero;C. Romero;J. Bacardit;V. M. Rivas;J. C. Fernández;F. Herrera

  • Affiliations:

  • University of Granada, Department of Computer Science and Artificial Intelligence, 18071, Granada, Spain;University of Oviedo, Department of Computer Science, 33204, Gijón, Spain;University of Granada, Department of Computer Science and Artificial Intelligence, 18071, Granada, Spain;University of Jaén, Department of Computer Science, 23071, Jaén, Spain;University of Córdoba, Department of Computer Sciences and Numerical Analysis, 14071, Córdoba, Spain;University Ramon Llull, Department of Computer Science, 08022, Barcelona, Spain;University of Oviedo, Department of Computer Science, 33204, Gijón, Spain;University of Córdoba, Department of Computer Sciences and Numerical Analysis, 14071, Córdoba, Spain;University of Nottingham, Department of Computer Science and Information Technology, NG8 1BB, Nottingham, UK;University of Jaén, Department of Computer Science, 23071, Jaén, Spain;University of Córdoba, Department of Computer Sciences and Numerical Analysis, 14071, Córdoba, Spain;University of Granada, Department of Computer Science and Artificial Intelligence, 18071, Granada, Spain

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
  • Year:
  • 2008

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Abstract

This paper introduces a software tool named KEEL which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL, as well as the integration of evolutionary learning techniques with different pre-processing techniques, allowing it to perform a complete analysis of any learning model in comparison to existing software tools. Moreover, KEEL has been designed with a double goal: research and educational.