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 25 articles , 1 standard article )

Showing results 1 to 20 of 25.
Sorted by year (citations)

1 2 next

  1. Jaworski, Maciej: Regression function and noise variance tracking methods for data streams with concept drift (2018)
  2. Montiel, Jacob; Read, Jesse; Bifet, Albert; Abdessalem, Talel: Scikit-multiflow: a multi-output streaming framework (2018)
  3. van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin: The online performance estimation framework: heterogeneous ensemble learning for data streams (2018)
  4. 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)
  5. Osojnik, Aljaž; Panov, Panče; Džeroski, Sašo: Multi-label classification via multi-target regression on data streams (2017)
  6. Srinivasan, Ashwin; Bain, Michael: An empirical study of on-line models for relational data streams (2017)
  7. Brzezinski, Dariusz; Piernik, Maciej: Structural XML classification in concept drifting data streams (2015) ioport
  8. Žliobaitė, Indrė; Bifet, Albert; Read, Jesse; Pfahringer, Bernhard; Holmes, Geoff: Evaluation methods and decision theory for classification of streaming data with temporal dependence (2015)
  9. Amini, Amineh; Wah, Teh Ying; Saboohi, Hadi: On density-based data streams clustering algorithms: a survey (2014) ioport
  10. Brzezinski, Dariusz; Stefanowski, Jerzy: Combining block-based and online methods in learning ensembles from concept drifting data streams (2014)
  11. Gama, João; Žliobaitė, Indrė; Bifet, Albert; Pechenizkiy, Mykola; Bouchachia, Abdelhamid: A survey on concept drift adaptation (2014)
  12. Jackowski, Konrad: Fixed-size ensemble classifier system evolutionarily adapted to a recurring context with an unlimited pool of classifiers (2014) ioport
  13. Miller, Zachary; Dickinson, Brian; Deitrick, William; Hu, Wei; Wang, Alex Hai: Twitter spammer detection using data stream clustering (2014) ioport
  14. Pears, Russel; Sakthithasan, Sripirakas; Koh, Yun Sing: Detecting concept change in dynamic data streams (2014)
  15. Albertini, Marcelo Keese; Fernandes De Mello, Rodrigo: Energy-based function to evaluate data stream clustering (2013)
  16. Bifet, Albert; Read, Jesse; Pfahringer, Bernhard; Holmes, Geoff; Žliobaitė, Indrė: CD-MOA: Change detection framework for massive online analysis (2013) ioport
  17. Deckert, Magdalena: Incremental rule-based learners for handling concept drift: an overview (2013) ioport
  18. Gama, João; Sebastião, Raquel; Rodrigues, Pedro Pereira: On evaluating stream learning algorithms (2013)
  19. Lughofer, Edwin: Flexible evolving fuzzy inference systems from data streams (FLEXFIS++) (2012) ioport
  20. Lughofer, Edwin; Eitzinger, Christian; Guardiola, Carlos: Online quality control with flexible evolving fuzzy systems (2012) ioport

1 2 next


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