WEKA: Waikato Environment for Knowledge Analysis. WEKA is a popular machine learning workbench with a development life of nearly two decades. This article provides an overview of the factors that we believe to be important to its success. Rather than focussing on the software’s functionality, we review aspects of project management and historical development decisions that likely had an impact on the uptake of the project.

References in zbMATH (referenced in 272 articles , 1 standard article )

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  1. Bain, Travaughn C.; Avila-Herrera, Juan F.; Subasi, Ersoy; Subasi, Munevver Mine: Logical analysis of multiclass data with relaxed patterns (2020)
  2. Granata, Ilaria; Guarracino, Mario R.; Kalyagin, Valery A.; Maddalena, Lucia; Manipur, Ichcha; Pardalos, Panos M.: Model simplification for supervised classification of metabolic networks (2020)
  3. Marrero-Ponce, Yovani; Teran, Julio E.; Contreras-Torres, Ernesto; García-Jacas, César R.; Perez-Castillo, Yunierkis; Cubillan, Nestor; Peréz-Giménez, Facundo; Valdés-Martini, José R.: LEGO-based generalized set of two linear algebraic 3D bio-macro-molecular descriptors: theory and validation by QSARs (2020)
  4. Xavier-Júnior, João C.; Freitas, Alex A.; Ludermir, Teresa B.; Feitosa-Neto, Antonino; Barreto, Cephas A. S.: An evolutionary algorithm for automated machine learning focusing on classifier ensembles: an improved algorithm and extended results (2020)
  5. Abpeykar, Shadi; Ghatee, Mehdi; Zare, Hadi: Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification (2019)
  6. Balázs, P.; Brunetti, S.: A (Q)-convexity vector descriptor for image analysis (2019)
  7. Bazan, Jan G.; Szczur, Adam; Skowron, Andrzej; Rzepko, Marian; Król, Paweł; Bajorek, Wojciech; Czarny, Wojciech: A classifier based on a decision tree with temporal cuts (2019)
  8. Bing Zhu; Zihan Gao; Junkai Zhao; Seppe K.L.M. van den Broucke: IRIC: An R library for binary imbalanced classification (2019) not zbMATH
  9. Brunetti, Sara; Balázs, Péter; Bodnár, Péter; Szűcs, Judit: A spatial convexity descriptor for object enlacement (2019)
  10. Cao, Lei; Lu, YanMeng; Li, ChuangQuan; Yang, Wei: Automatic segmentation of pathological glomerular basement membrane in transmission electron microscopy images with random forest stacks (2019)
  11. Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd: \textttOpenML: an \textttRpackage to connect to the machine learning platform openml (2019)
  12. Dubitzky, Werner; Lopes, Philippe; Davis, Jesse; Berrar, Daniel: The Open International Soccer Database for machine learning (2019)
  13. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  14. Gao, Can; Lai, Zhihui; Zhou, Jie; Wen, Jiajun; Wong, Wai Keung: Granular maximum decision entropy-based monotonic uncertainty measure for attribute reduction (2019)
  15. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
  16. Haq, Anam; Wilk, Szymon; Abelló, Alberto: Fusion of clinical data: a case study to predict the type of treatment of bone fractures (2019)
  17. Huang, Zongyan; England, Matthew; Wilson, David J.; Bridge, James; Davenport, James H.; Paulson, Lawrence C.: Using machine learning to improve cylindrical algebraic decomposition (2019)
  18. Jia, Xiuyi; Deng, Zhao; Min, Fan; Liu, Dun: Three-way decisions based feature fusion for Chinese irony detection (2019)
  19. Lindauer, Marius; van Rijn, Jan N.; Kotthoff, Lars: The algorithm selection competitions 2015 and 2017 (2019)
  20. Livieris, Ioannis E.; Kanavos, Andreas; Tampakas, Vassilis; Pintelas, Panagiotis: A weighted voting ensemble self-labeled algorithm for the detection of lung abnormalities from X-rays (2019)

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Further publications can be found at: http://www.cs.waikato.ac.nz/ml/publications.html