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KEEL is a software tool to assess
evolutionary algorithms for Data Mining problems including
regression, classification, clustering, pattern mining and so on.
It contains a big collection of classical knowledge extraction algorithms,
preprocessing techniques (instance selection, feature selection, discretization, imputation methods
for missing values, etc.), Computational Intelligence based learning algorithms, including evolutionary
rule learning algorithms based on different approaches (Pittsburgh, Michigan and IRL, ...), and hybrid
models such as genetic fuzzy systems, evolutionary neural networks, etc.
It allows us to perform a complete analysis of any learning model in comparison to existing ones,
including a statistical test module for comparison.
Moreover, KEEL has been designed with a double goal: research and educational.
For a detailed description, see the section 'KEEL description' on the
left menu.
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KEEL description paper:
J. Alcalá-Fdez, L. Sánchez, S. García, M.J. del Jesus, S. Ventura,
J.M. Garrell, J. Otero, C. Romero, J. Bacardit, V.M. Rivas, J.C. Fernández, F. Herrera.
KEEL: A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems.
Soft Computing 13:3 (2009) 307-318, doi: 10.1007/s00500-008-0323-y.
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