KEEL: a software tool to assess evolutionary algorithms for data mining problems

By Jesús Alcalá-Fdez, L. Sánchez, Salvador Garcia, M. J. Jesús, Sebastián Ventura, Josep Maria Garrell i Guiu, José Otero, Cristóbal Romero, Jaume Bacardit, Víctor Manuel Rivas Santos and 2 more. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2008

1,515 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/0468532d28499ef38287afd7557dd62b802ee85b

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In Artificial Intelligence

Citations in the corpus

A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability (2009, methodology): “These algorithms are provided by the KEEL software (Alcalá-Fdez et al. 2009), which includes updated versions of these GBML methods.”
Design and Implementation of a Learning Analytics Toolkit for Teachers (2012, methodology): “, 2009) or Keel (Alcalá-Fdez et al., 2009), to start their own studies.”
A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning (2011, methodology): “In these experiments, we compare the proposed approach with other ten methods, which are available in the KEEL software tool [ 61 ].”
An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons (2008, methodology): “All classifiers are available in KEEL software (Alcalá-Fdez et al., 2008).”
A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning (2013, methodology): “Additionally, implementations of these algorithms in Java can be found in KEEL software [114], [115].”
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, methodology): “We must point out that these algorithms are available within the KEEL software tool [4].”