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 131 articles , 1 standard article )

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  1. 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)
  2. Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo: Portfolio approaches for constraint optimization problems (2016)
  3. Arias, Jacinto; Gamez, Jose A.; Nielsen, Thomas D.; Puerta, Jose M.: A scalable pairwise class interaction framework for multidimensional classification (2016)
  4. 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)
  5. Bischl, Bernd; Lang, Michel; Kotthoff, Lars; Schiffner, Julia; Richter, Jakob; Studerus, Erich; Casalicchio, Giuseppe; Jones, Zachary M.: Mlr: machine learning in $\bold R$ (2016)
  6. 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)
  7. Janning, Ruth; Schatten, Carlotta; Schmidt-Thieme, Lars: Perceived task-difficulty recognition from log-file information for the use in adaptive intelligent tutoring systems (2016)
  8. Jessup, Elizabeth; Motter, Pate; Norris, Boyana; Sood, Kanika: Performance-based numerical solver selection in the lighthouse framework (2016)
  9. Li, Weiwei; Huang, Zhiqiu; Jia, Xiuyi; Cai, Xinye: Neighborhood based decision-theoretic rough set models (2016)
  10. Martínez, Ana M.; Webb, Geoffrey I.; Chen, Shenglei; Zaidi, Nayyar A.: Scalable learning of Bayesian network classifiers (2016)
  11. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  12. Read, Jesse; Reutemann, Peter; Pfahringer, Bernhard; Holmes, Geoff: MEKA: A multi-label/multi-target extension to WEKA (2016)
  13. Rieck, Konrad; Wressnegger, Christian: Harry: a tool for measuring string similarity (2016)
  14. Wu, Xuesong; Tang, Haoran; Guan, Aoran; Sun, Feng; Wang, Hui; Shu, Jie: Finding gastric cancer related genes and clinical biomarkers for detection based on gene-gene interaction network (2016)
  15. Dhurandhar, Amit; Sankaranarayanan, Karthik: Improving classification performance through selective instance completion (2015)
  16. Fernandez-Lozano, Carlos; Cuiñas, Rubén F.; Seoane, José A.; Fernández-Blanco, Enrique; Dorado, Julian; Munteanu, Cristian R.: Classification of signaling proteins based on molecular star graph descriptors using machine learning models (2015)
  17. Heger, Jens; Hildebrandt, Torsten; Reiter, Bernd-Scholz: Dispatching rule selection with Gaussian processes (2015)
  18. Maravalle, Maurizio; Ricca, Federica; Simeone, Bruno; Spinelli, Vincenzo: Carpal tunnel syndrome automatic classification: electromyography vs. ultrasound imaging (2015)
  19. Meng, Jun; Li, Rui; Luan, Yushi: Classification by integrating plant stress response gene expression data with biological knowledge (2015)
  20. Ozturk, Gurkan; Bagirov, Adil M.; Kasimbeyli, Refail: An incremental piecewise linear classifier based on polyhedral conic separation (2015)

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