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 184 articles , 1 standard article )

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  1. Ansótegui, Carlos; Bonet, Maria Luisa; Giráldez-Cru, Jesús; Levy, Jordi: Structure features for SAT instances classification (2017)
  2. Bagarello, Fabio; Cinà, Marco; Gargano, Francesco: Projector operators in clustering (2017)
  3. Barrett, Samuel; Rosenfeld, Avi; Kraus, Sarit; Stone, Peter: Making friends on the fly: cooperating with new teammates (2017)
  4. Chlebowski, Szymon; Komosinski, Maciej; Kups, Adam: Automated generation of erotetic search scenarios: classification, optimization, and knowledge extraction (2017)
  5. Chou, Chun-An; Bonates, Tibérius O.; Lee, Chungmok; Chaovalitwongse, Wanpracha Art: Multi-pattern generation framework for logical analysis of data (2017)
  6. Escuín, David; Polo, Lorena; Ciprés, David: On the comparison of inventory replenishment policies with time-varying stochastic demand for the paper industry (2017)
  7. Komendantskaya, Ekaterina; Heras, Jónathan: Proof mining with dependent types (2017)
  8. Kotthoff, Lars; Thornton, Chris; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA (2017)
  9. Kunze, Lars; Beetz, Michael: Envisioning the qualitative effects of robot manipulation actions using simulation-based projections (2017)
  10. Lombardi, Michele; Milano, Michela; Bartolini, Andrea: Empirical decision model learning (2017)
  11. Nápoles, Gonzalo; Falcon, Rafael; Papageorgiou, Elpiniki; Bello, Rafael; Vanhoof, Koen: Rough cognitive ensembles (2017)
  12. Picek, Stjepan; Heuser, Annelie; Jovic, Alan; Legay, Axel: Climbing down the hierarchy: hierarchical classification for machine learning side-channel attacks (2017)
  13. Piotr Szymanski: A scikit-based Python environment for performing multi-label classification (2017) arXiv
  14. 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
  15. Ramirez-Amaro, Karinne; Beetz, Michael; Cheng, Gordon: Transferring skills to humanoid robots by extracting semantic representations from observations of human activities (2017)
  16. Ting, Kai Ming; Washio, Takashi; Wells, Jonathan R.; Aryal, Sunil: Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors (2017)
  17. Youssef, Abdou: Part-of-math tagging and applications (2017)
  18. 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)
  19. Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo: Portfolio approaches for constraint optimization problems (2016)
  20. Arias, Jacinto; Gamez, Jose A.; Nielsen, Thomas D.; Puerta, Jose M.: A scalable pairwise class interaction framework for multidimensional classification (2016)

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