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

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  1. da Cruz Asmus, Tiago; Dimuro, Graçaliz Pereira; Bedregal, Benjamín; Sanz, José Antonio; Pereira, Sidnei jun.; Bustince, Humberto: General interval-valued overlap functions and interval-valued overlap indices (2020)
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  7. Bing Zhu; Zihan Gao; Junkai Zhao; Seppe K.L.M. van den Broucke: IRIC: An R library for binary imbalanced classification (2019) not zbMATH
  8. Chen, Min-Rong; Zeng, Guo-Qiang; Lu, Kang-Di: A many-objective population extremal optimization algorithm with an adaptive hybrid mutation operation (2019)
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  19. Abellán, Joaquín; Castellano, Javier G.; Mantas, Carlos J.: A new robust classifier on noise domains: bagging of Credal C4.5 trees (2017)
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