Citing scikit-ika

Please use the following BibTex entry if you would like to cite scikit-ika for your publications:

PEARL: Probabilistic Exact Adaptive Random Forest with Lossy Counting for Data Streams here:

@inproceedings{wu2020pearl,
title={PEARL: Probabilistic Exact Adaptive Random Forest with Lossy Counting for Data Streams},
author={Wu, Ocean and Koh, Yun Sing and Dobbie, Gillian and Lacombe, Thomas},
booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining},
pages={17--30},
year={2020},
organization={Springer}}

Here is an incomplete list of peer-reviewed papers:

• Wu, O., Koh, Y. S., Dobbie, G., & Lacombe, T. (2020, May). PEARL: Probabilistic Exact Adaptive Random Forest with Lossy Counting for Data Streams. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 17-30). Springer, Cham.

• Lacombe, T., Koh, Y. S., Dobbie, G., & Wu, O. A Meta-Learning Approach for Automated Hyperparameter Tuning in Evolving Data Streams. 2021 International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1-8.

• Wu, O., Koh, Y. S., Dobbie, G., & Lacombe, T. Nacre: Proactive Recurrent Concept Drift Detection in Data Streams. 2021 International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1-8.

• Wu, O., Koh, Y. S., Dobbie, G., & Lacombe, T. (2021). Transfer Learning with Adaptive Online TrAdaBoost for Data Streams. Proceedings of The 13th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 157:1017-1032.

• Wu, O., Koh, Y. S., Dobbie, G., & Lacombe, T. Probabilistic exact adaptive random forest for recurrent concepts in data streams. Int J Data Sci Anal 13, 17–32 (2022).