KEEL

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 95 articles , 1 standard article )

Showing results 1 to 20 of 95.
Sorted by year (citations)

1 2 3 4 5 next

  1. van Engelen, Jesper E.; Hoos, Holger H.: A survey on semi-supervised learning (2020)
  2. Benítez-Peña, Sandra; Blanquero, Rafael; Carrizosa, Emilio; Ramírez-Cobo, Pepa: On support vector machines under a multiple-cost scenario (2019)
  3. Bing Zhu; Zihan Gao; Junkai Zhao; Seppe K.L.M. van den Broucke: IRIC: An R library for binary imbalanced classification (2019) not zbMATH
  4. De Miguel, Laura; Gómez, Daniel; Rodríguez, J. Tinguaro; Montero, Javier; Bustince, Humberto; Dimuro, Graçaliz P.; Sanz, José Antonio: General overlap functions (2019)
  5. Li, Wei: Matrix adaptation evolution strategy with multi-objective optimization for multimodal optimization (2019)
  6. Wang, Zhe; Cao, Chenjie: Cascade interpolation learning with double subspaces and confidence disturbance for imbalanced problems (2019)
  7. Abdul Aziz, Nor Hidayati; Ibrahim, Zuwairie; Ab Aziz, Nor Azlina; Mohamad, Mohd Saberi; Watada, Junzo: Single-solution simulated Kalman filter algorithm for global optimisation problems (2018)
  8. 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
  9. Muñoz, Mario A.; Villanova, Laura; Baatar, Davaatseren; Smith-Miles, Kate: Instance spaces for machine learning classification (2018)
  10. Abellán, Joaquín; Castellano, Javier G.; Mantas, Carlos J.: A new robust classifier on noise domains: bagging of Credal C4.5 trees (2017)
  11. Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore: PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison (2017) arXiv
  12. Zuo, Cili; Wu, Lianghong; Zeng, Zhao-Fu; Wei, Hua-Liang: Stochastic fractal based multiobjective fruit fly optimization (2017)
  13. Ángel M. García, Francisco Charte, Pedro González, Cristóbal J. Carmona, María J. del Jesus: Subgroup Discovery with Evolutionary Fuzzy Systems in R: The SDEFSR Package (2016) not zbMATH
  14. 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)
  15. Gámez, Juan Carlos; García, David; González, Antonio; Pérez, Raúl: Ordinal classification based on the sequential covering strategy (2016)
  16. Gao, Shangce; Wang, Yirui; Cheng, Jiujun; Inazumi, Yasuhiro; Tang, Zheng: Ant colony optimization with clustering for solving the dynamic location routing problem (2016)
  17. Luo, Qifang; Zhang, Sen; Li, Zhiming; Zhou, Yongquan: A novel complex-valued encoding grey wolf optimization algorithm (2016)
  18. Manukyan, Artür; Ceyhan, Elvan: Classification of imbalanced data with a geometric digraph family (2016)
  19. Ngufor, Che; Wojtusiak, Janusz: Extreme logistic regression (2016)
  20. 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

1 2 3 4 5 next