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. 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)
  2. 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)
  3. Derrac, Joaquín; García, Salvador; Herrera, Francisco: Fuzzy nearest neighbor algorithms: taxonomy, experimental analysis and prospects (2014)
  4. Elgibreen, Hebah; Aksoy, Mehmet: RULES-IT: incremental transfer learning with RULES family (2014)
  5. 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)
  6. Galar, Mikel; Fernández, Alberto; Barrenechea, Edurne; Herrera, Francisco: Empowering difficult classes with a similarity-based aggregation in multi-class classification problems (2014)
  7. García, David; González, Antonio; Pérez, Raúl: A feature construction approach for genetic iterative rule learning algorithm (2014)
  8. Gong, Wenyin; Cai, Zhihua; Liang, Dingwen: Engineering optimization by means of an improved constrained differential evolution (2014)
  9. Leyva, Enrique; Caises, Yoel; González, Antonio; Pérez, Raúl: On the use of meta-learning for instance selection: an architecture and an experimental study (2014)
  10. 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)
  11. Macià, Núria; Bernadó-Mansilla, Ester: Towards UCI+: a mindful repository design (2014)
  12. Maldonado, Sebastián; López, Julio: Imbalanced data classification using second-order cone programming support vector machines (2014)
  13. Martín, D.; Rosete, A.; Alcalá-Fdez, J.; Herrera, F.: QAR-CIP-NSGA-II: a new multi-objective evolutionary algorithm to mine quantitative association rules (2014)
  14. Otero, José; Sánchez, Luciano; Couso, Inés; Palacios, Ana: Bootstrap analysis of multiple repetitions of experiments using an interval-valued multiple comparison procedure (2014)
  15. Park, So-Youn; Lee, Ju-Jang: An efficient differential evolution using speeded-up k-nearest neighbor estimator (2014)
  16. Sancho-Asensio, Andreu; Orriols-Puig, Albert; Golobardes, Elisabet: Robust on-line neural learning classifier system for data stream classification tasks (2014)
  17. Cano, Alberto; Zafra, Amelia; Ventura, Sebastián: An interpretable classification rule mining algorithm (2013)
  18. Derrac, J.; Verbiest, N.; García, S.; Cornelis, C.; Herrera, F.: On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection (2013)
  19. Franco, María A.; Krasnogor, Natalio; Bacardit, Jaume: GAssist vs. BioHEL: critical assessment of two paradigms of genetics-based machine learning (2013)
  20. López, Victoria; Fernández, Alberto; García, Salvador; Palade, Vasile; Herrera, Francisco: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics (2013)

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