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 291 articles , 1 standard article )

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  1. Zhang, Dan; Chen, Hua-Dong; Zulfiqar, Hasan; Yuan, Shi-Shi; Huang, Qin-Lai; Zhang, Zhao-Yue; Deng, Ke-Jun: iBLP: an XGBoost-based predictor for identifying bioluminescent proteins (2021)
  2. Anthony D. Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer: MLJ: A Julia package for composable Machine Learning (2020) arXiv
  3. Bain, Travaughn C.; Avila-Herrera, Juan F.; Subasi, Ersoy; Subasi, Munevver Mine: Logical analysis of multiclass data with relaxed patterns (2020)
  4. Granata, Ilaria; Guarracino, Mario R.; Kalyagin, Valery A.; Maddalena, Lucia; Manipur, Ichcha; Pardalos, Panos M.: Model simplification for supervised classification of metabolic networks (2020)
  5. Granata, Ilaria; Guarracino, Mario Rosario; Maddalena, Lucia; Manipur, Ichcha: Network distances for weighted digraphs (2020)
  6. Gupta, Vipin; Bhattacharyya, Abhijit; Pachori, Ram Bilas: Automated identification of epileptic seizures from EEG signals using FBSE-EWT method (2020)
  7. Jacobs, Kayla; Itai, Alon; Wintner, Shuly: Acronyms: identification, expansion and disambiguation (2020)
  8. Kangas, Kustaa; Koivisto, Mikko; Salonen, Sami: A faster tree-decomposition based algorithm for counting linear extensions (2020)
  9. Marrero-Ponce, Yovani; Teran, Julio E.; Contreras-Torres, Ernesto; García-Jacas, César R.; Perez-Castillo, Yunierkis; Cubillan, Nestor; Peréz-Giménez, Facundo; Valdés-Martini, José R.: LEGO-based generalized set of two linear algebraic 3D bio-macro-molecular descriptors: theory and validation by QSARs (2020)
  10. Nguyen, Thi Thanh Sang; Do, Pham Minh Thu: Classification optimization for training a large dataset with naïve Bayes (2020)
  11. Shakerin, Farhad; Gupta, Gopal: White-box induction from SVM models: explainable AI with logic programming (2020)
  12. Singh, Shivani; Shreevastava, Shivam; Som, Tanmoy; Somani, Gaurav: A fuzzy similarity-based rough set approach for attribute selection in set-valued information systems (2020)
  13. Xavier-Júnior, João C.; Freitas, Alex A.; Ludermir, Teresa B.; Feitosa-Neto, Antonino; Barreto, Cephas A. S.: An evolutionary algorithm for automated machine learning focusing on classifier ensembles: an improved algorithm and extended results (2020)
  14. Ye, Fei-Fei; Wang, Suhui; Nicholl, Peter; Yang, Long-Hao; Wang, Ying-Ming: Extended belief rule-based model for environmental investment prediction with indicator ensemble selection (2020)
  15. Abpeykar, Shadi; Ghatee, Mehdi; Zare, Hadi: Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification (2019)
  16. Balázs, P.; Brunetti, S.: A (Q)-convexity vector descriptor for image analysis (2019)
  17. Bazan, Jan G.; Szczur, Adam; Skowron, Andrzej; Rzepko, Marian; Król, Paweł; Bajorek, Wojciech; Czarny, Wojciech: A classifier based on a decision tree with temporal cuts (2019)
  18. Bentkowska, Urszula; Bazan, Jan G.; Rząsa, Wojciech; Zarȩba, Lech: Application of interval-valued aggregation to optimization problem of (k)-NN classifiers for missing values case (2019)
  19. Bing Zhu; Zihan Gao; Junkai Zhao; Seppe K.L.M. van den Broucke: IRIC: An R library for binary imbalanced classification (2019) not zbMATH
  20. Brunetti, Sara; Balázs, Péter; Bodnár, Péter; Szűcs, Judit: A spatial convexity descriptor for object enlacement (2019)

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