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Introduction to the Experimental Design in the Data Mining Tool KEEL
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Author(s): J. Alcalá-Fdez (University of Granada, Spain), F. Herrera (University of Jaén, Spain), S. García (University of Oviedo, Spain), M.J. del Jesus (University Ramon Llull, Spain), L. Sánchez (University of Huelva, Spain), Ester Bernadó-Mansilla (TecnoCampus, Spain), A. Peregrín (University of Huelva, Spain)and S. Ventura (University of Córdoba, Spain)
Copyright: 2010
Pages: 25
Source title:
Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technologies
Source Author(s)/Editor(s): Leon Shyue-Liang Wang (National University of Kaohsiung, Taiwan)and Tzung-Pei Hong (National University of Kaohsiung, Taiwan)
DOI: 10.4018/978-1-61520-757-2.ch001
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Abstract
KEEL is a Data Mining software tool to assess the behaviour of evolutionary learning algorithms in particular and soft computing algorithms in general for different kinds of Data Mining problems including as regression, classification, clustering, pattern mining and so on. It allows us to perform a complete analysis of some learning model in comparison to existing ones, including a statistical test module for comparison. In this chapter the authors will provide a complete description of KEEL, the kind of problems and algorithms implemented, and they will present a case of study for showing the experimental design and statistical analysis that they can do with KEEL.
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