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

نویسندگان

  • Jesús Alcalá-Fdez
  • Luciano Sánchez
  • Salvador García
  • María José del Jesús
  • Sebastián Ventura
  • Josep Maria Garrell i Guiu
  • José Otero
  • Cristóbal Romero
  • Jaume Bacardit
  • Víctor Manuel Rivas Santos
  • Juan Carlos Fernández
  • Francisco Herrera
چکیده

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.

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عنوان ژورنال:
  • Soft Comput.

دوره 13  شماره 

صفحات  -

تاریخ انتشار 2009