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. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  2. Gao, Can; Lai, Zhihui; Zhou, Jie; Wen, Jiajun; Wong, Wai Keung: Granular maximum decision entropy-based monotonic uncertainty measure for attribute reduction (2019)
  3. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
  4. Haq, Anam; Wilk, Szymon; Abelló, Alberto: Fusion of clinical data: a case study to predict the type of treatment of bone fractures (2019)
  5. Harrison, Kyle Robert; Ombuki-Berman, Beatrice M.; Engelbrecht, Andries P.: A parameter-free particle swarm optimization algorithm using performance classifiers (2019)
  6. Huang, Zongyan; England, Matthew; Wilson, David J.; Bridge, James; Davenport, James H.; Paulson, Lawrence C.: Using machine learning to improve cylindrical algebraic decomposition (2019)
  7. Kangas, Kustaa; Koivisto, Mikko; Salonen, Sami: A faster tree-decomposition based algorithm for counting linear extensions (2019)
  8. Lindauer, Marius; van Rijn, Jan N.; Kotthoff, Lars: The algorithm selection competitions 2015 and 2017 (2019)
  9. Livieris, Ioannis E.; Kanavos, Andreas; Tampakas, Vassilis; Pintelas, Panagiotis: A weighted voting ensemble self-labeled algorithm for the detection of lung abnormalities from X-rays (2019)
  10. Li, Weiwei; Jia, Xiuyi; Wang, Lu; Zhou, Bing: Multi-objective attribute reduction in three-way decision-theoretic rough set model (2019)
  11. Mabonzo, V. D.; Moungabio, F. Malonga; Ngoma, D. V. Pongui: Efficiency of data mining in classroom teaching mathematics (2019)
  12. Ma, Xi-Ao; Zhao, Xue Rong: Cost-sensitive three-way class-specific attribute reduction (2019)
  13. Michael Hahsler; Matthew Piekenbrock; Derek Doran: dbscan: Fast Density-Based Clustering with R (2019) not zbMATH
  14. Michel Lang, Martin Binder, Jakob Richter, Patrick Schratz, Florian Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff, Bernd Bischl: mlr3: A modern object-oriented machine learning framework in R (2019) not zbMATH
  15. Pulina, Luca; Seidl, Martina: The 2016 and 2017 QBF solvers evaluations (QBFEVAL’16 and QBFEVAL’17) (2019)
  16. Reis, Marcelo S.; Estrela, Gustavo; Ferreira, Carlos Eduardo; Barrera, Junior: Optimal Boolean lattice-based algorithms for the U-curve optimization problem (2019)
  17. Vluymans, Sarah; Mac Parthaláin, Neil; Cornelis, Chris; Saeys, Yvan: Weight selection strategies for ordered weighted average based fuzzy rough sets (2019)
  18. Williams, Lowri; Arribas-Ayllon, Michael; Artemiou, Andreas; Spasić, Irena: Comparing the utility of different classification schemes for emotive language analysis (2019)
  19. 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)
  20. Adriano Rivolli; Andre C. P. L. F. de Carvalho: The utiml Package: Multi-label Classification in R (2018) not zbMATH

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