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 40 articles , 1 standard article )

Showing results 1 to 20 of 40.
Sorted by year (citations)

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  1. Cano, Alberto; Krawczyk, Bartosz: Kappa updated ensemble for drifting data stream mining (2020)
  2. Grzenda, Maciej; Gomes, Heitor Murilo; Bifet, Albert: Delayed labelling evaluation for data streams (2020)
  3. Osojnik, Aljaž; Panov, Panče; Džeroski, Sašo: Incremental predictive clustering trees for online semi-supervised multi-target regression (2020)
  4. 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)
  5. 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)
  6. Guimarães, Victor; Paes, Aline; Zaverucha, Gerson: Online probabilistic theory revision from examples with ProPPR (2019)
  7. Nguyen, Thi Thu Thuy; Nguyen, Tien Thanh; Sharma, Rabi; Liew, Alan Wee-Chung: A lossless online Bayesian classifier (2019)
  8. Razmjoo, Alaleh; Xanthopoulos, Petros; Zheng, Qipeng Phil: Feature importance ranking for classification in mixed online environments (2019)
  9. Shahparast, Homeira; Mansoori, Eghbal G.: Developing an online general type-2 fuzzy classifier using evolving type-1 rules (2019)
  10. Geilke, Michael; Karwath, Andreas; Frank, Eibe; Kramer, Stefan: Online estimation of discrete, continuous, and conditional joint densities using classifier chains (2018)
  11. Jaworski, Maciej: Regression function and noise variance tracking methods for data streams with concept drift (2018)
  12. Lughofer, Edwin: Robust data-driven fault detection in dynamic process environments using discrete event systems (2018)
  13. Montiel, Jacob; Read, Jesse; Bifet, Albert; Abdessalem, Talel: Scikit-multiflow: a multi-output streaming framework (2018)
  14. van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin: The online performance estimation framework: heterogeneous ensemble learning for data streams (2018)
  15. Yang, Rui; Xu, Shuliang; Feng, Lin: An ensemble extreme learning machine for data stream classification (2018)
  16. Osojnik, Aljaž; Panov, Panče; Džeroski, Sašo: Multi-label classification via multi-target regression on data streams (2017)
  17. Pietruczuk, Lena; Rutkowski, Leszek; Jaworski, Maciej; Duda, Piotr: How to adjust an ensemble size in stream data mining? (2017)
  18. Srinivasan, Ashwin; Bain, Michael: An empirical study of on-line models for relational data streams (2017)
  19. Zhai, Tingting; Gao, Yang; Wang, Hao; Cao, Longbing: Classification of high-dimensional evolving data streams via a resource-efficient online ensemble (2017)
  20. 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)

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