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.

References in zbMATH (referenced in 83 articles , 1 standard article )

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  1. Francisco Charte, Antonio J. Rivera, David Charte, María J. del Jesus, Francisco Herrera: Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository (2018) arXiv
  2. Muñoz, Mario A.; Villanova, Laura; Baatar, Davaatseren; Smith-Miles, Kate: Instance spaces for machine learning classification (2018)
  3. Abellán, Joaquín; Castellano, Javier G.; Mantas, Carlos J.: A new robust classifier on noise domains: bagging of Credal C4.5 trees (2017)
  4. Zuo, Cili; Wu, Lianghong; Zeng, Zhao-Fu; Wei, Hua-Liang: Stochastic fractal based multiobjective fruit fly optimization (2017)
  5. Fernández, Alberto; Elkano, Mikel; Galar, Mikel; Sanz, José Antonio; Alshomrani, Saleh; Bustince, Humberto; Herrera, Francisco: Enhancing evolutionary fuzzy systems for multi-class problems: distance-based relative competence weighting with truncated confidences (DRCW-TC) (2016)
  6. Gámez, Juan Carlos; García, David; González, Antonio; Pérez, Raúl: Ordinal classification based on the sequential covering strategy (2016)
  7. Manukyan, Artür; Ceyhan, Elvan: Classification of imbalanced data with a geometric digraph family (2016)
  8. López, Victoria; del Río, Sara; Benítez, José Manuel; Herrera, Francisco: Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data (2015) ioport
  9. Wang, Zhe; Fan, Qi; Ke, Sheng; Gao, Daqi: Structural multiple empirical kernel learning (2015)
  10. Antonelli, Michela; Ducange, Pietro; Marcelloni, Francesco: A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers (2014)
  11. Derrac, Joaquín; García, Salvador; Herrera, Francisco: Fuzzy nearest neighbor algorithms: taxonomy, experimental analysis and prospects (2014) ioport
  12. Elgibreen, Hebah; Aksoy, Mehmet: RULES-IT: incremental transfer learning with RULES family (2014) ioport
  13. Gacto, M.J.; Galende, M.; Alcalá, R.; Herrera, F.: $\mathrmMETSK-HD^e$: a multiobjective evolutionary algorithm to learn accurate TSK-fuzzy systems in high-dimensional and large-scale regression problems (2014) ioport
  14. Galar, Mikel; Fernández, Alberto; Barrenechea, Edurne; Herrera, Francisco: Empowering difficult classes with a similarity-based aggregation in multi-class classification problems (2014)
  15. García, David; González, Antonio; Pérez, Raúl: A feature construction approach for genetic iterative rule learning algorithm (2014)
  16. Gong, Wenyin; Cai, Zhihua; Liang, Dingwen: Engineering optimization by means of an improved constrained differential evolution (2014)
  17. Leyva, Enrique; Caises, Yoel; González, Antonio; Pérez, Raúl: On the use of meta-learning for instance selection: an architecture and an experimental study (2014) ioport
  18. López, Victoria; Fernández, Alberto; Herrera, Francisco: On the importance of the validation technique for classification with imbalanced datasets: addressing covariate shift when data is skewed (2014) ioport
  19. Lu, Qiang; Han, Qing-Long; Liu, Shirong: A finite-time particle swarm optimization algorithm for odor source localization (2014)
  20. Macià, Núria; Bernadó-Mansilla, Ester: Towards UCI+: a mindful repository design (2014) ioport

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