Implementation and integration of algorithms into the KEEL data-mining software tool

Authors:
Alberto Fernández;Julián Luengo;Joaquin Derrac;Jesús Alcalá-Fdez;Francisco Herrera
Affiliations:
Dept. of Computer Science and A.I., University of Granada;Dept. of Computer Science and A.I., University of Granada;Dept. of Computer Science and A.I., University of Granada;Dept. of Computer Science and A.I., University of Granada;Dept. of Computer Science and A.I., University of Granada
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

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Abstract

This work is related to the KEEL (Knowledge Extraction based on Evolutionary Learning) tool, a non-commercial software that supports data management, design of experiments and an educational section. The KEEL software tool is devoted to assess evolutionary algorithms for Data Mining problems including regression, classification, clustering, pattern mining and so on. These features implies an advantage for the research and educational field. The aim of this contribution is to present some guidelines for including new algorithms in KEEL, helping the researchers to make their methods easily accessible for other authors and to compare the results of many approaches already included within the KEEL software. By providing a source code template, the developer does not need to take into account the basic requirements of the KEEL software tool, and he or she has only to focus in the designing and encoding of his or hers approach.