KEEL Project

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.

Relation between KEEL and JCLEC projects is due to the fact that many of the evolutionary learning algorithms contained in KEEL have been implemented using the JCLEC framework. More information about the KEEL can be found at the project webpage http://www.keel.es.