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
- Developed by Peter Reutemann
- https://moa.cms.waikato.ac.nz/using-moa-interactively-with-the-new-java-shell-tool/
- 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:
Cheers,
The MOA Team