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 154 articles , 1 standard article )

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  1. Barrett, Samuel; Rosenfeld, Avi; Kraus, Sarit; Stone, Peter: Making friends on the fly: cooperating with new teammates (2017)
  2. Escuín, David; Polo, Lorena; Ciprés, David: On the comparison of inventory replenishment policies with time-varying stochastic demand for the paper industry (2017)
  3. Piotr Szymanski: A scikit-based Python environment for performing multi-label classification (2017) arXiv
  4. Ralf Mikut, Andreas Bartschat, Wolfgang Doneit, Jorge Angel Gonzalez Ordiano, Benjamin Schott, Johannes Stegmaier, Simon Waczowicz, Markus Reischl: The MATLAB Toolbox SciXMiner: User’s Manual and Programmer’s Guide (2017) arXiv
  5. Ting, Kai Ming; Washio, Takashi; Wells, Jonathan R.; Aryal, Sunil: Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors (2017)
  6. Al-Mamun, M.A.; Farid, D.M.; Ravenhil, L.; Hossain, M.A.; Fall, C.; Bass, R.: An \itin silico model to demonstrate the effects of Maspin on cancer cell dynamics (2016)
  7. Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo: Portfolio approaches for constraint optimization problems (2016)
  8. Arias, Jacinto; Gamez, Jose A.; Nielsen, Thomas D.; Puerta, Jose M.: A scalable pairwise class interaction framework for multidimensional classification (2016)
  9. Bertolazzi, P.; Felici, G.; Festa, P.; Fiscon, G.; Weitschek, E.: Integer programming models for feature selection: new extensions and a randomized solution algorithm (2016)
  10. Bischl, Bernd; Kerschke, Pascal; Kotthoff, Lars; Lindauer, Marius; Malitsky, Yuri; Fréchette, Alexandre; Hoos, Holger; Hutter, Frank; Leyton-Brown, Kevin; Tierney, Kevin; Vanschoren, Joaquin: ASlib: a benchmark library for algorithm selection (2016)
  11. Bischl, Bernd; Lang, Michel; Kotthoff, Lars; Schiffner, Julia; Richter, Jakob; Studerus, Erich; Casalicchio, Giuseppe; Jones, Zachary M.: Mlr: machine learning in $\bold R$ (2016)
  12. de Campos, Cassio P.; Corani, Giorgio; Scanagatta, Mauro; Cuccu, Marco; Zaffalon, Marco: Learning extended tree augmented naive structures (2016)
  13. 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)
  14. Gámez, Juan Carlos; García, David; González, Antonio; Pérez, Raúl: Ordinal classification based on the sequential covering strategy (2016)
  15. Janning, Ruth; Schatten, Carlotta; Schmidt-Thieme, Lars: Perceived task-difficulty recognition from log-file information for the use in adaptive intelligent tutoring systems (2016) MathEduc
  16. Jessup, Elizabeth; Motter, Pate; Norris, Boyana; Sood, Kanika: Performance-based numerical solver selection in the Lighthouse framework (2016)
  17. Khabsa, Madian; Elmagarmid, Ahmed; Ilyas, Ihab; Hammady, Hossam; Ouzzani, Mourad: Learning to identify relevant studies for systematic reviews using random forest and external information (2016)
  18. Li, Weiwei; Huang, Zhiqiu; Jia, Xiuyi; Cai, Xinye: Neighborhood based decision-theoretic rough set models (2016)
  19. Martínez, Ana M.; Webb, Geoffrey I.; Chen, Shenglei; Zaidi, Nayyar A.: Scalable learning of Bayesian network classifiers (2016)
  20. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)

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