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 304 articles , 1 standard article )

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  1. Piccolo, Stephen R.; Frey, Lewis J.: ML-flex: a flexible toolbox for performing classification analyses in parallel (2012)
  2. Rabhi, Fethi A.; Yao, Lawrence; Guabtni, Adnene: ADAGE: a framework for supporting user-driven ad-hoc data analysis processes (2012) ioport
  3. Read, Jesse; Bifet, Albert; Holmes, Geoff; Pfahringer, Bernhard: Scalable and efficient multi-label classification for evolving data streams (2012) ioport
  4. Rieck, Konrad; Wressnegger, Christian; Bikadorov, Alexander: Sally: a tool for embedding strings in vector spaces (2012) ioport
  5. Tehrani, Ali Fallah; Cheng, Weiwei; Dembczyński, Krzysztof; Hüllermeier, Eyke: Learning monotone nonlinear models using the Choquet integral (2012)
  6. Tirkaz, Caglar; Yanikoglu, Berrin; Sezgin, T. Metin: Sketched symbol recognition with auto-completion (2012) ioport
  7. Vanschoren, Joaquin; Blockeel, Hendrik; Pfahringer, Bernhard; Holmes, Geoffrey: Experiment databases (2012)
  8. Vatolkin, Igor; Preuß, Mike; Rudolph, Günter; Eichhoff, Markus; Weihs, Claus: Multi-objective evolutionary feature selection for instrument recognition in polyphonic audio mixtures (2012) ioport
  9. Wei, Dengping; Wang, Ting; Wang, Ji: A logistic regression model for semantic web service matchmaking (2012) ioport
  10. Zhang, Junbo; Li, Tianrui; Ruan, Da; Gao, Zizhe; Zhao, Chengbing: A parallel method for computing rough set approximations (2012) ioport
  11. Zheng, Fei; Webb, Geoffrey I.; Suraweera, Pramuditha; Zhu, Liguang: Subsumption resolution: an efficient and effective technique for semi-naive Bayesian learning (2012)
  12. Zimek, Arthur; Schubert, Erich; Kriegel, Hans-peter: A survey on unsupervised outlier detection in high-dimensional numerical data (2012)
  13. Alphonse, Erick; Girschick, Tobias; Buchwald, Fabian; Kramer, Stefan: A numerical refinement operator based on multi-instance learning (2011)
  14. Aymerich, F. X.; Alonso, J.; Cabañas, M. E.; Comabella, M.; Sobrevilla, P.; Rovira, A.: Decision tree based fuzzy classifier of (^1)H magnetic resonance spectra from cerebrospinal fluid samples (2011) ioport
  15. Bagheri, Ebrahim; Gasevic, Dragan: Assessing the maintainability of software product line feature models using structural metrics (2011) ioport
  16. Beliakov, Gleb; James, Simon: Citation-based journal ranks: the use of fuzzy measures (2011)
  17. Hansen, Pierre; Meyer, Christophe: A new column generation algorithm for logical analysis of data (2011)
  18. Japkowicz, Nathalie; Shah, Mohak: Evaluating learning algorithms. A classification perspective (2011)
  19. Kalgi, Srihari; Gosar, Chirag; Gawde, Prasad; Ramakrishnan, Ganesh; Gada, Kekin; Iyer, Chander; Kiran, T. V. S.; Srinivasan, Ashwin: BET: An inductive logic programming workbench (2011) ioport
  20. Kovacs, Tim; Egginton, Robert: On the analysis and design of software for reinforcement learning, with a survey of existing systems (2011) ioport

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