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.
['J. Alcalá-Fdez', 'Laura Elisabet Gómez Sánchez', 'Salvador del Barrio García', 'M. J. del Jesus', 'S. Ventura', 'Josep Maria Garrell', 'J. Otero', 'Carlos Romero', 'Jaume Bacardit', 'V. M. Rivas', 'J. C. Fernández', 'Francisco Herrera']
KEEL: a software tool to assess evolutionary algorithms for data mining problems