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 155 articles , 1 standard article )

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  1. Moshe Sipper, Tomer Halperin, Itai Tzruia, Achiya Elyasaf: Sipper EC-KitY Evolutionary Python (2022) arXiv
  2. Örnek, Bülent Nafi; Aydemir, Salih Berkan; Düzenli, Timur; Özak, Bilal: A novel version of slime mould algorithm for global optimization and real world engineering problems. Enhanced slime mould algorithm (2022)
  3. Alanazi, Fadhah Amer: A mixture of regular vines for multiple dependencies (2021)
  4. Alanazi, Fadhah Amer: Sequential truncation of (R)-vine copula mixture model for high-dimensional datasets (2021)
  5. Aminian, Ehsan; Ribeiro, Rita P.; Gama, João: Chebyshev approaches for imbalanced data streams regression models (2021)
  6. Chen, Zhi; Duan, Jiang; Kang, Li; Qiu, Guoping: A hybrid data-level ensemble to enable learning from highly imbalanced dataset (2021)
  7. Geng, Xiaojiao; Liang, Yan; Jiao, Lianmeng: EARC: evidential association rule-based classification (2021)
  8. Korani, Wael; Mouhoub, Malek: Review on nature-inspired algorithms (2021)
  9. Koziarski, Michał; Bellinger, Colin; Woźniak, Michał: RB-CCR: radial-based combined cleaning and resampling algorithm for imbalanced data classification (2021)
  10. Li, Guoquan; Yang, Linxi; Wu, Zhiyou; Wu, Changzhi: D.C. programming for sparse proximal support vector machines (2021)
  11. Li, Wei; Gong, Wenyin: Differential evolution with quasi-reflection-based mutation (2021)
  12. Nikolaos Anastasopoulos, Ioannis G. Tsoulos, Alexandros Tzallas: GenClass: A parallel tool for data classification based on Grammatical Evolution (2021) not zbMATH
  13. Shahee, Shaukat Ali; Ananthakumar, Usha: An overlap sensitive neural network for class imbalanced data (2021)
  14. Soltanzadeh, Paria; Hashemzadeh, Mahdi: RCSMOTE: range-controlled synthetic minority over-sampling technique for handling the class imbalance problem (2021)
  15. Suárez, Juan Luis; García, Salvador; Herrera, Francisco: Ordinal regression with explainable distance metric learning based on ordered sequences (2021)
  16. Tomaž Hočevar, Blaž Zupan, Jonna Stålring: Conformal Prediction with Orange (2021) not zbMATH
  17. Chakraborty, Saptarshi; Paul, Debolina; Das, Swagatam: Hierarchical clustering with optimal transport (2020)
  18. 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)
  19. González-Almagro, Germán; Luengo, Julián; Cano, José-Ramón; García, Salvador: DILS: constrained clustering through dual iterative local search (2020)
  20. Kadam, Vinod Jagannath; Jadhav, Shivajirao Manikrao: Performance analysis of hyperparameter optimization methods for ensemble learning with small and medium sized medical datasets (2020)

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