You find my codes, written in Java, for the MOA framework in this repository. My drift detectors may be used with the MOA framework for detecting concept drift in envolving data streams.

Tutorial on Setting Up Everything.

Please cite the following papers, or thesis, if you plan to use FHDDM, FHDDMS, and MDDMs:

1. Pesaranghader, Ali. "A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams", Ph.D. Dissertation, Université d'Ottawa/University of Ottawa, 2018. DOI: http:https://dx.doi.org/10.20381/ruor-22444
2. Pesaranghader, Ali, et al. "Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams", Machine Learning Journal, 2018.
   Pre-print available at: https://arxiv.org/abs/1709.02457, DOI: https://doi.org/10.1007/s10994-018-5719-z
3. Pesaranghader, Ali, et al. "Fast Hoeffding Drift Detection Method for Evolving Data Streams", European Conference on Machine Learning, 2016.
   Pre-print available at: http:https://iwera.ir/~ali/papers/ecml2016.pdf, DOI: https://doi.org/10.1007/978-3-319-46227-1_7
4. Pesaranghader, Ali, et al. "McDiarmid Drift Detection Methods for Evolving Data Streams", International Joint Conference on Neural Networks, 2018.
   Pre-print available at: https://arxiv.org/abs/1710.02030, DOI: https://doi.org/10.1109/IJCNN.2018.8489260

_{Ali Pesaranghader © 2020++
Under MIT License}