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 219 articles , 1 standard article )

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  1. Abpeykar, Shadi; Ghatee, Mehdi; Zare, Hadi: Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification (2019)
  2. Li, An-Da; He, Zhen; Wang, Qing; Zhang, Yang: Key quality characteristics selection for imbalanced production data using a two-phase bi-objective feature selection method (2019)
  3. Abdulrahman, Salisu Mamman; Brazdil, Pavel; van Rijn, Jan N.; Vanschoren, Joaquin: Speeding up algorithm selection using average ranking and active testing by introducing runtime (2018)
  4. Aggarwal, Charu C.: Machine learning for text (2018)
  5. Amin, Talha; Moshkov, Mikhail: Totally optimal decision rules (2018)
  6. Cerutti, Federico; Vallati, Mauro; Giacomin, Massimiliano: On the impact of configuration on abstract argumentation automated reasoning (2018)
  7. Czajka, Łukasz; Kaliszyk, Cezary: Hammer for Coq: automation for dependent type theory (2018)
  8. de Caigny, Arno; Coussement, Kristof; de Bock, Koen W.: A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees (2018)
  9. Du, Wen Sheng; Hu, Bao Qing: A fast heuristic attribute reduction approach to ordered decision systems (2018)
  10. Filice, Simone; Castellucci, Giuseppe; Da San Martino, Giovanni; Moschitti, Alessandro; Croce, Danilo; Basili, Roberto: KeLP: a kernel-based learning platform (2018)
  11. Gudivada, Venkat N.; Arbabifard, Kamyar: Open-source libraries, application frameworks, and workflow systems for NLP (2018)
  12. Livieris, Ioannis E.; Kotsilieris, Theodore; Dimopoulos, Ioannis; Pintelas, Panagiotis: Decision support software for forecasting patient’s length of stay (2018)
  13. Malone, Brandon; Kangas, Kustaa; Järvisalo, Matti; Koivisto, Mikko; Myllymäki, Petri: Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction (2018)
  14. Montiel, Jacob; Read, Jesse; Bifet, Albert; Abdessalem, Talel: Scikit-multiflow: a multi-output streaming framework (2018)
  15. Muñoz, Mario A.; Villanova, Laura; Baatar, Davaatseren; Smith-Miles, Kate: Instance spaces for machine learning classification (2018)
  16. Olier, Ivan; Sadawi, Noureddin; Bickerton, G. Richard; Vanschoren, Joaquin; Grosan, Crina; Soldatova, Larisa; King, Ross D.: Meta-QSAR: a large-scale application of meta-learning to drug design and discovery (2018)
  17. Pawel Kasprowski; Katarzyna Harezlak: ETCAL - A versatile and extendable library for eye tracker calibration (2018) not zbMATH
  18. Saha, Sriparna: Enhancing point symmetry-based distance for data clustering (2018)
  19. Tunga, Burcu: A hybrid algorithm with cluster analysis in modelling high dimensional data (2018)
  20. van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin: The online performance estimation framework: heterogeneous ensemble learning for data streams (2018)

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