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

J. Alcalá-Fdez, L. Sánchez, S. García, M. J. del Jesus, S. Ventura, J. M. Garrell, J. Otero, C. Romero, J. Bacardit and V. M. Rivas, et al.

From the issue entitled "Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta"

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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.

Keywords  Computer-based education - Data mining - Evolutionary computation - Experimental design - Graphical programming - Java - Knowledge extraction - Machine learning

Supported by the Spanish Ministry of Science and Technology under Projects TIN-2005-08386-C05-(01, 02, 03, 04 and 05). The work of Dr. Bacardit is also supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant GR/T07534/01.

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