MOA

MOA (Massive Online Analysis). MOA is the most popular open source framework for data stream mining, with a very active growing community (blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.


References in zbMATH (referenced in 48 articles , 1 standard article )

Showing results 1 to 20 of 48.
Sorted by year (citations)

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  1. Aminian, Ehsan; Ribeiro, Rita P.; Gama, João: Chebyshev approaches for imbalanced data streams regression models (2021)
  2. Bakirov, Rashid; Fay, Damien; Gabrys, Bogdan: Automated adaptation strategies for stream learning (2021)
  3. Bernardo, Alessio; Della Valle, Emanuele: VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams (2021)
  4. Halstead, Ben; Koh, Yun Sing; Riddle, Patricia; Pears, Russel; Pechenizkiy, Mykola; Bifet, Albert: Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency (2021)
  5. Krawczyk, Bartosz: Tensor decision trees for continual learning from drifting data streams (2021)
  6. Lughofer, Edwin: Improving the robustness of recursive consequent parameters learning in evolving neuro-fuzzy systems (2021)
  7. Shaker, Ammar; Hüllermeier, Eyke: TSK-Streams: learning TSK fuzzy systems for regression on data streams (2021)
  8. Zhao, Xin; Barber, Stuart; Taylor, Charles C.; Milan, Zoka: Interval forecasts based on regression trees for streaming data (2021)
  9. Cano, Alberto; Krawczyk, Bartosz: Kappa updated ensemble for drifting data stream mining (2020)
  10. Grzenda, Maciej; Gomes, Heitor Murilo; Bifet, Albert: Delayed labelling evaluation for data streams (2020)
  11. Osojnik, Aljaž; Panov, Panče; Džeroski, Sašo: Incremental predictive clustering trees for online semi-supervised multi-target regression (2020)
  12. Souza, Vinicius M. A.; dos Reis, Denis M.; Maletzke, André G.; Batista, Gustavo E. A. P. A.: Challenges in benchmarking stream learning algorithms with real-world data (2020)
  13. Ud Din, Salah; Shao, Junming; Kumar, Jay; Ali, Waqar; Liu, Jiaming; Ye, Yu: Online reliable semi-supervised learning on evolving data streams (2020)
  14. Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd: \textttOpenML: an \textttRpackage to connect to the machine learning platform openml (2019)
  15. Guimarães, Victor; Paes, Aline; Zaverucha, Gerson: Online probabilistic theory revision from examples with ProPPR (2019)
  16. Nguyen, Thi Thu Thuy; Nguyen, Tien Thanh; Sharma, Rabi; Liew, Alan Wee-Chung: A lossless online Bayesian classifier (2019)
  17. Razmjoo, Alaleh; Xanthopoulos, Petros; Zheng, Qipeng Phil: Feature importance ranking for classification in mixed online environments (2019)
  18. Shahparast, Homeira; Mansoori, Eghbal G.: Developing an online general type-2 fuzzy classifier using evolving type-1 rules (2019)
  19. Geilke, Michael; Karwath, Andreas; Frank, Eibe; Kramer, Stefan: Online estimation of discrete, continuous, and conditional joint densities using classifier chains (2018)
  20. Jaworski, Maciej: Regression function and noise variance tracking methods for data streams with concept drift (2018)

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Further publications can be found at: http://moa.cms.waikato.ac.nz/publications/