New Release of MOA 19.04

We’ve made a new release of MOA 19.04

The new features of this release are:

  • A new separate tab for our new Experimenter.
    • Developed by Alberto Verdecia Cabrera and Isvani Frías Blanco
  • LITE Mode: a new default mode that displays only the classifiers needed to start learning MOA, that hides the complexity of having all the classifiers (STANDARD mode)
    • Developed by Corey Sterling
  • A new separate tab for scripting, using jshell-scripting widget
  • Preview table is included now in all tabs
    • 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.
  • Extremely Fast Decision Tree (EFDT)
    • Chaitanya Manapragada, Geoffrey I. Webb, Mahsa Salehi: Extremely Fast Decision Tree. KDD 2018
  • Adaptive Random Forests For Regression
    • Heitor Murilo Gomes, Jean Paul Barddal, Luis Eduardo Boiko Ferreira, Albert Bifet: Adaptive random forests for data stream regression. ESANN 2018
  • Added one-class classifiers
    • AutoEncoder
    • Streaming Half-Space Trees (Swee Chuan Tan, Kai Ming Ting, Fei Tony Liu: Fast Anomaly Detection for Streaming Data. IJCAI 2011)
    • Nearest Neighbour Description (David M. J. Tax: One-Class Classification: Concept-learning in the absence of counter-examples, Delft University of Technology, 2001)
    • Richard Hugh Moulton, “Clustering to Improve One-Class Classifier Performance in Data Streams,” Master’s thesis, University of Ottawa, 2018
  • Added ability to shuffle cached multi-label ARFF files
    • Developed by Henry Gouk

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


The MOA Team