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 190 articles , 1 standard article )

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  1. Amin, Talha; Moshkov, Mikhail: Totally optimal decision rules (2018)
  2. Cerutti, Federico; Vallati, Mauro; Giacomin, Massimiliano: On the impact of configuration on abstract argumentation automated reasoning (2018)
  3. Tunga, Burcu: A hybrid algorithm with cluster analysis in modelling high dimensional data (2018)
  4. Ansótegui, Carlos; Bonet, Maria Luisa; Giráldez-Cru, Jesús; Levy, Jordi: Structure features for SAT instances classification (2017)
  5. Bagarello, Fabio; Cinà, Marco; Gargano, Francesco: Projector operators in clustering (2017)
  6. Barrett, Samuel; Rosenfeld, Avi; Kraus, Sarit; Stone, Peter: Making friends on the fly: cooperating with new teammates (2017)
  7. Chlebowski, Szymon; Komosinski, Maciej; Kups, Adam: Automated generation of erotetic search scenarios: classification, optimization, and knowledge extraction (2017)
  8. Chou, Chun-An; Bonates, Tibérius O.; Lee, Chungmok; Chaovalitwongse, Wanpracha Art: Multi-pattern generation framework for logical analysis of data (2017)
  9. Dehzangi, Abdollah; López, Yosvany; Lal, Sunil Pranit; Taherzadeh, Ghazaleh; Michaelson, Jacob; Sattar, Abdul; Tsunoda, Tatsuhiko; Sharma, Alok: PSSM-Suc: accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction (2017)
  10. Escuín, David; Polo, Lorena; Ciprés, David: On the comparison of inventory replenishment policies with time-varying stochastic demand for the paper industry (2017)
  11. Komendantskaya, Ekaterina; Heras, Jónathan: Proof mining with dependent types (2017)
  12. 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)
  13. Kunze, Lars; Beetz, Michael: Envisioning the qualitative effects of robot manipulation actions using simulation-based projections (2017)
  14. Lombardi, Michele; Milano, Michela; Bartolini, Andrea: Empirical decision model learning (2017)
  15. Nagy, Ivan; Suzdaleva, Evgenia: Algorithms and programs of dynamic mixture estimation. Unified approach to different types of components (2017)
  16. Nápoles, Gonzalo; Falcon, Rafael; Papageorgiou, Elpiniki; Bello, Rafael; Vanhoof, Koen: Rough cognitive ensembles (2017)
  17. Picek, Stjepan; Heuser, Annelie; Jovic, Alan; Legay, Axel: Climbing down the hierarchy: hierarchical classification for machine learning side-channel attacks (2017)
  18. Piotr Szymanski: A scikit-based Python environment for performing multi-label classification (2017) arXiv
  19. 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
  20. Ramirez-Amaro, Karinne; Beetz, Michael; Cheng, Gordon: Transferring skills to humanoid robots by extracting semantic representations from observations of human activities (2017)

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