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 111 articles , 1 standard article )

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  1. Korani, Wael; Mouhoub, Malek: Review on nature-inspired algorithms (2021)
  2. Li, Guoquan; Yang, Linxi; Wu, Zhiyou; Wu, Changzhi: D.C. programming for sparse proximal support vector machines (2021)
  3. Li, Wei; Gong, Wenyin: Differential evolution with quasi-reflection-based mutation (2021)
  4. Nikolaos Anastasopoulos, Ioannis G. Tsoulos, Alexandros Tzallas: GenClass: A parallel tool for data classification based on Grammatical Evolution (2021) not zbMATH
  5. Tomaž Hočevar, Blaž Zupan, Jonna Stålring: Conformal Prediction with Orange (2021) not zbMATH
  6. 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)
  7. González-Almagro, Germán; Luengo, Julián; Cano, José-Ramón; García, Salvador: DILS: constrained clustering through dual iterative local search (2020)
  8. Liu, Songbai; Yu, Qiyuan; Lin, Qiuzhen; Tan, Kay Chen: An adaptive clustering-based evolutionary algorithm for many-objective optimization problems (2020)
  9. van Engelen, Jesper E.; Hoos, Holger H.: A survey on semi-supervised learning (2020)
  10. Wang, Bing-Chuan; Feng, Yun; Li, Han-Xiong: Individual-dependent feasibility rule for constrained differential evolution (2020)
  11. Wang, Jieting; Qian, Yuhua; Li, Feijiang: Learning with mitigating random consistency from the accuracy measure (2020)
  12. Benítez-Peña, Sandra; Blanquero, Rafael; Carrizosa, Emilio; Ramírez-Cobo, Pepa: On support vector machines under a multiple-cost scenario (2019)
  13. Bing Zhu; Zihan Gao; Junkai Zhao; Seppe K.L.M. van den Broucke: IRIC: An R library for binary imbalanced classification (2019) not zbMATH
  14. Chen, Min-Rong; Zeng, Guo-Qiang; Lu, Kang-Di: A many-objective population extremal optimization algorithm with an adaptive hybrid mutation operation (2019)
  15. De Miguel, Laura; Gómez, Daniel; Rodríguez, J. Tinguaro; Montero, Javier; Bustince, Humberto; Dimuro, Graçaliz P.; Sanz, José Antonio: General overlap functions (2019)
  16. Li, Wei: Matrix adaptation evolution strategy with multi-objective optimization for multimodal optimization (2019)
  17. Nguyen, Bac; Ferri, Francesc J.; Morell, Carlos; De Baets, Bernard: An efficient method for clustered multi-metric learning (2019)
  18. Wang, Zhe; Cao, Chenjie: Cascade interpolation learning with double subspaces and confidence disturbance for imbalanced problems (2019)
  19. Zhang, Yongshan; Wu, Jia; Cai, Zhihua; Du, Bo; Yu, Philip S.: An unsupervised parameter learning model for RVFL neural network (2019)
  20. 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)

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