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 36 articles , 1 standard article )

Showing results 1 to 20 of 36.
Sorted by year (citations)

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  1. Cano, Alberto; Krawczyk, Bartosz: Kappa updated ensemble for drifting data stream mining (2020)
  2. 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)
  3. Guimarães, Victor; Paes, Aline; Zaverucha, Gerson: Online probabilistic theory revision from examples with ProPPR (2019)
  4. Razmjoo, Alaleh; Xanthopoulos, Petros; Zheng, Qipeng Phil: Feature importance ranking for classification in mixed online environments (2019)
  5. Shahparast, Homeira; Mansoori, Eghbal G.: Developing an online general type-2 fuzzy classifier using evolving type-1 rules (2019)
  6. Geilke, Michael; Karwath, Andreas; Frank, Eibe; Kramer, Stefan: Online estimation of discrete, continuous, and conditional joint densities using classifier chains (2018)
  7. Jaworski, Maciej: Regression function and noise variance tracking methods for data streams with concept drift (2018)
  8. Montiel, Jacob; Read, Jesse; Bifet, Albert; Abdessalem, Talel: Scikit-multiflow: a multi-output streaming framework (2018)
  9. van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin: The online performance estimation framework: heterogeneous ensemble learning for data streams (2018)
  10. Yang, Rui; Xu, Shuliang; Feng, Lin: An ensemble extreme learning machine for data stream classification (2018)
  11. Gomes, Heitor M.; Bifet, Albert; Read, Jesse; Barddal, Jean Paul; Enembreck, Fabrício; Pfharinger, Bernhard; Holmes, Geoff; Abdessalem, Talel: Adaptive random forests for evolving data stream classification (2017)
  12. Osojnik, Aljaž; Panov, Panče; Džeroski, Sašo: Multi-label classification via multi-target regression on data streams (2017)
  13. Pietruczuk, Lena; Rutkowski, Leszek; Jaworski, Maciej; Duda, Piotr: How to adjust an ensemble size in stream data mining? (2017)
  14. Srinivasan, Ashwin; Bain, Michael: An empirical study of on-line models for relational data streams (2017)
  15. Zhai, Tingting; Gao, Yang; Wang, Hao; Cao, Longbing: Classification of high-dimensional evolving data streams via a resource-efficient online ensemble (2017)
  16. Song, Ge; Ye, Yunming; Zhang, Haijun; Xu, Xiaofei; Lau, Raymond Y. K.; Liu, Feng: Dynamic clustering forest: an ensemble framework to efficiently classify textual data stream with concept drift (2016)
  17. Webb, Geoffrey I.; Hyde, Roy; Cao, Hong; Nguyen, Hai Long; Petitjean, Francois: Characterizing concept drift (2016)
  18. Brzezinski, Dariusz; Piernik, Maciej: Structural XML classification in concept drifting data streams (2015) ioport
  19. Žliobaitė, Indrė; Bifet, Albert; Read, Jesse; Pfahringer, Bernhard; Holmes, Geoff: Evaluation methods and decision theory for classification of streaming data with temporal dependence (2015)
  20. Amini, Amineh; Wah, Teh Ying; Saboohi, Hadi: On density-based data streams clustering algorithms: a survey (2014) ioport

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