New Release of MOA 14.11

We’ve made a new release of MOA 14.11.

The new features of this release are:

  • Lazy kNN methods.
    • Albert Bifet, Bernhard Pfahringer, Jesse Read, Geoff Holmes: Efficient data stream classification via probabilistic adaptive windows. SAC 2013: 801-806
  • SGDMultiClass for multi-class SGD learning.
  • OnlineSmoothBoost
    • Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu:An Online Boosting Algorithm with Theoretical Justifications. ICML 2012
  • ReplacingMissingValuesFilter: a filter to replace missing values by Manuel Martin Salvador.
  • HDDM Concept Drift detector
    • I. Frias-Blanco, J. del Campo-Avila, G. Ramos-Jimenez, R. Morales-Bueno, A. Ortiz-Diaz, and Y. Caballero-Mota, Online and non-parametric drift detection methods based on Hoeffding’s bound, IEEE Transactions on Knowledge and Data Engineering, 2014.
  • SeqDriftChangeDetector Concept Drift detector
    • Pears, R., Sakthithasan, S., & Koh, Y. (2014). Detecting concept change in dynamic data streams. Machine Learning, 97(3), 259-293.
  • Updates:
    • SGD, HoeffdingOptionTree, HAT, FIMTDD, Change Detectors, and DACC

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

MOA Machine Learning for Streams


The MOA Team