New Release of MOA 17.06

We’ve made a new release of MOA 17.06

The new features of this release are:

  • SAMKnn:
    • Viktor Losing, Barbara Hammer, Heiko Wersing: KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. ICDM 2016: 291-300
  • Adaptive Random Forest:
    • Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabricio Enembreck, Bernhard Pfharinger, Geoff Holmes, Talel Abdessalem. Adaptive random forests for evolving data stream classification. In Machine Learning, Springer, 2017.
  • Blast:
    • Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren: Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams. ICDM 2015: 1003-1008
  • Prequential AUC:
    • Dariusz Brzezinski, Jerzy Stefanowski: Prequential AUC: properties of the area under the ROC curve for data streams with concept drift. Knowl. Inf. Syst. 52(2): 531-562 (2017)
  • D-Stream:
    • Yixin Chen, Li Tu: Density-based clustering for real-time stream data. KDD 2007: 133-142
  • IADEM:
    • Isvani Inocencio Frías Blanco, José del Campo-Ávila, Gonzalo Ramos-Jiménez, André Carvalho, Agustín Alejandro Ortiz Díaz, Rafael Morales Bueno: Online adaptive decision trees based on concentration inequalities. Knowl.-Based Syst. 104: 179-194 (2016)
  • RCD Classifier:
    • Gonçalves Jr, Paulo Mauricio, and Roberto Souto Maior De Barros: RCD: A recurring concept drift framework. Pattern Recognition Letters 34.9 (2013): 1018-1025.
  • ADAGrad:
    • John C. Duchi, Elad Hazan, Yoram Singer: Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. Journal of Machine Learning Research 12: 2121-2159 (2011)
  • Dynamic Weighted Majority:
    • J. Zico Kolter, Marcus A. Maloof: Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts. Journal of Machine Learning Research 8: 2755-2790 (2007)
  • StepD:
    • Kyosuke Nishida, Koichiro Yamauchi: Detecting Concept Drift Using Statistical Testing. Discovery Science 2007: 264-269
  • LearnNSE:
    • Ryan Elwell, Robi Polikar: Incremental Learning of Concept Drift in Nonstationary Environments. IEEE Trans. Neural Networks 22(10): 1517-1531 (2011)
  • ASSETS Generator:
    • Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck, Bernhard Pfahringer, Albert Bifet: On Dynamic Feature Weighting for Feature Drifting Data Streams. ECML/PKDD (2) 2016: 129-144
  • SineGenerator and MixedGenerator:
    • Joao Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues: Learning with Drift Detection. SBIA 2004: 286-295
  • AMRulesMultiLabelLearnerSemiSuper (Semi-supervised Multi-target regressor):
    • Ricardo Sousa, Joao Gama: Online Semi-supervised Learning for Multi-target Regression in Data Streams Using AMRules. IDA 2016: 123-133
  • AMRulesMultiLabelClassifier (Multi-label classifier):
    • Ricardo Sousa, Joao Gama: Online Multi-label Classification with Adaptive Model Rules. CAEPIA 2016: 58-67
  • WeightedMajorityFeatureRanking, MeritFeatureRanking and BasicFeatureRanking (Feature ranking methods)
    • Joao Duarte, Joao Gama: Feature ranking in hoeffding algorithms for regression. SAC 2017: 836-841
  • AMRulesMultiTargetRegressor (Multi-target regressor):
    • Joao Duarte, Joao Gama, Albert Bifet: Adaptive Model Rules From High-Speed Data Streams. TKDD 10(3): 30:1-30:22 (2016)

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

MOA Machine Learning for Streams


The MOA Team