New Release of MOA 18.06

We’ve made a new release of MOA 18.06

The new features of this release are:

  • A new separate tab for Active Learning
    • Developed at the Otto-von-Guericke-University Magdeburg, Germany, by Tuan Pham Minh, Tim Sabsch and Cornelius Styp von Rekowski, and supervised by Daniel Kottke and Georg Krempl.
  • Reactive Drift Detection Method (RDDM)
    • Roberto S. M. Barros, Danilo R. L. Cabral, Paulo M. Goncalves Jr., and Silas G. T. C. Santos: RDDM: Reactive Drift Detection Method.. In Expert Systems With Applications 90C (2017) pp. 344-355.
  • Adaptable Diversity-based Online Boosting (ADOB)
    • Silas G. T. C. Santos, Paulo M. Goncalves Jr., Geyson D. S. Silva, and Roberto S. M. Barros: Speeding Up Recovery from Concept Drifts.In Machine Learning and Knowledge Discovery in Databases, ECML/PKDD 2014, Part III, LNCS 8726, pp. 179-194. 09/2014.
  • Boosting-like Online Learning Ensemble (BOLE)
    • Roberto Souto Maior de Barros, Silas Garrido T. de Carvalho Santos, and Paulo Mauricio Goncalves Jr.: A Boosting-like Online Learning Ensemble. In Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, 2016./li>
  • TextGenerator that simulates tweets to do sentiment analysis
  • EvaluateMultipleClusterings, which automates multiple EvaluateClustering tasks; and WriteMultipleStreamsToARFF, which automates multiple WriteStreamToARFFFile tasks.
    • Richard Hugh Moulton
  • Meta-generator to append irrelevant features, Imbalanced Stream, F1, Precision and recall
    • Jean Paul Barddal

You can find the download link for this release on the MOA homepage:

MOA Machine Learning for Streams


The MOA Team