WEKA

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. Lichtenwalter, Ryan N.; Chawla, Nitesh V.: LPmade: link prediction made easy (2011)
  2. Read, Jesse; Pfahringer, Bernhard; Holmes, Geoff; Frank, Eibe: Classifier chains for multi-label classification (2011) ioport
  3. Stelle, Diogo; Barioni, Maria C.; Scott, Luis P.: Using data mining to identify structural rules in proteins (2011)
  4. Vanneschi, Leonardo; Codecasa, Daniele; Mauri, Giancarlo: A comparative study of four parallel and distributed PSO methods (2011) ioport
  5. Archetti, Francesco; Giordani, Ilaria; Vanneschi, Leonardo: Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset (2010)
  6. Bonissone, Piero; Cadenas, José M.; Garrido, M. Carmen; Díaz-Valladares, R. Andrés: A fuzzy random forest (2010) ioport
  7. Bouckaert, Remco R.; Frank, Eibe; Hall, Mark A.; Holmes, Geoffrey; Pfahringer, Bernhard; Reutemann, Peter; Witten, Ian H.: WEKA -- experiences with a Java open-source project (2010)
  8. Lievens, Stijn; De Baets, Bernard: Supervised ranking in the WEKA environment (2010)
  9. Misselwitz, Benjamin; Strittmatter, Gerhard; Periaswamy, Balamurugan; Schlumberger, Markus C.; Rout, Samuel; Horvath, Peter; Kozak, Karol; Hardt, Wolf-Dietrich: Enhanced cellclassifier: a multi-class classification tool for microscopy images (2010) ioport
  10. Nettleton, David F.; Orriols-Puig, Albert; Fornells, Albert: A study of the effect of different types of noise on the precision of supervised learning techniques (2010) ioport
  11. Vázquez, S. G.; Barreira, N.; Penedo, M. G.; Ortega, M.; Pose-Reino, A.: Improvements in retinal vessel clustering techniques: Towards the automatic computation of the arterio venous ratio (2010)
  12. Hall, Mark; Frank, Eibe; Holmes, Geoffrey; Pfahringer, Bernhard; Reutemann, Peter; Witten, Ian H.: The WEKA data mining software: an update (2009) ioport
  13. Haraguchi, Kazuya; Hong, Seok-Hee; Nagamochi, Hiroshi: Bipartite graph representation of multiple decision table classifiers (2009)
  14. Hornik, Kurt; Buchta, Christian; Zeileis, Achim: Open-source machine learning: R meets Weka (2009)
  15. Kordík, Pavel: GAME -- hybrid self-organizing modeling system based on GMDH (2009)
  16. Rokach, Lior: Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography (2009)
  17. Vanneschi, Leonardo; Archetti, Francesco; Castelli, Mauro; Giordani, Ilaria: Classification of oncologic data with genetic programming (2009) ioport
  18. Wu, Chih-Hung; Tsai, Chiung-Hui: Robust classification for spam filtering by back-propagation neural networks using behavior-based features (2009) ioport
  19. Bonates, T. O.; Hammer, Peter L.; Kogan, A.: Maximum patterns in datasets (2008)
  20. Greensmith, Julie; Feyereisl, Jan; Aickelin, Uwe: The DCA: SOMe comparison. A comparative study between two biologically inspired algorithms (2008) ioport

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