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A NOVEL CONCEPT DRIFT DETECTION METHOD BASED ON THE PARTIAL DEPENDENCE PROFILE DISPARITY INDEX

This repository consists of the supplemental materials of a conference paper.

If you use the paper or codes, please cite it:

@INPROCEEDINGS{,
  author={Dar, Ugur and Cavus, Mustafa},
  booktitle={Proceedings Book of the 7th International Researchers, Statisticians and Young Statisticians Congress}, 
  title={A novel concept drift detection method based on the partial dependence profile disparity index}, 
  year={2023},
  pages={164-173},
  ISBN={978-625-8368-61-1}}

References

  • Przemyslaw Biecek and Tomasz Burzykowski. Explanatory Model Analysis. Chapman and Hall/CRC, New York, 2021. ISBN: 9780367135591. Link

  • Katarzyna Kobylińska, Mateusz Krzyziński, Rafał Machowicz, Mariusz Adamek, and Przemysław Biecek. Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data. arXiv preprint arXiv:2308.11446, 2023. Link

  • Bilge Celik, Prabhant Singh, Joaquin Vanschoren. Online AutoML: an adaptive AutoML framework for online learning. Machine Learning, Springer Science and Business Media LLC, 112(6): 1897--1921, 2022. DOI

  • Masciopinto, F. 2019. “Comparison of concept drift detectors in a health-care facility dataset to detect behavioral drifts”.

  • Demšar, J., and Bosnić, Z. 2018. “Detecting concept drift in data streams using model explanation”.Expert Systems with Applications,92, 546-559.

  • Thorne WB (2019). posterdown: An R Package Built to Generate Reproducible Conference Posters for the Academic and Professional World Where Powerpoint and Pages Just Won't Cut It. R package version 1.0, https://github.com/brentthorne/posterdown.

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