AUC estimation and concept drift detection for imbalanced data streams with multiple classes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

AUC estimation and concept drift detection for imbalanced data streams with multiple classes. / Wang, Shuo; Minku, Leandro.

Proceedings of the International Joint Conference on Neural Networks (IJCNN), World Congress on Computational Intelligence, 2020. IEEE Computer Society Press, 2020. 9207377 (Proceedings of International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Wang, S & Minku, L 2020, AUC estimation and concept drift detection for imbalanced data streams with multiple classes. in Proceedings of the International Joint Conference on Neural Networks (IJCNN), World Congress on Computational Intelligence, 2020., 9207377, Proceedings of International Joint Conference on Neural Networks, IEEE Computer Society Press, IEEE International Joint Conference on Neural Networks (IJCNN), 2020 , Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/IJCNN48605.2020.9207377

APA

Wang, S., & Minku, L. (2020). AUC estimation and concept drift detection for imbalanced data streams with multiple classes. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), World Congress on Computational Intelligence, 2020 [9207377] (Proceedings of International Joint Conference on Neural Networks). IEEE Computer Society Press. https://doi.org/10.1109/IJCNN48605.2020.9207377

Vancouver

Wang S, Minku L. AUC estimation and concept drift detection for imbalanced data streams with multiple classes. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), World Congress on Computational Intelligence, 2020. IEEE Computer Society Press. 2020. 9207377. (Proceedings of International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN48605.2020.9207377

Author

Wang, Shuo ; Minku, Leandro. / AUC estimation and concept drift detection for imbalanced data streams with multiple classes. Proceedings of the International Joint Conference on Neural Networks (IJCNN), World Congress on Computational Intelligence, 2020. IEEE Computer Society Press, 2020. (Proceedings of International Joint Conference on Neural Networks).

Bibtex

@inproceedings{313ff4ad969d4e2cbef6877042ef8caa,
title = "AUC estimation and concept drift detection for imbalanced data streams with multiple classes",
abstract = "Online class imbalance learning deals with data streams having very skewed class distributions. When learning from data streams, concept drift is one of the major challenges that deteriorate the classification performance. Although several approaches have been recently proposed to overcome concept drift in imbalanced data, they are all limited to two-class cases. Multi-class imbalance imposes additional challenges in concept drift detection and performance evaluation, such as a more severe imbalanced distribution and the limited choice of performance measures. This paper extends AUC for evaluating classifiers on multi-class imbalanced data in online learning scenarios. The proposed metrics, PMAUC, WAUC and EWAUC, are studied through comprehensive experiments, focusing on their characteristics on time-changing data streams and whether and how they can be used to detect concept drift. The AUC-based metrics show effectiveness in detecting concept drift in a variety of artificial data streams and a real-world data application with multiple classes. In particular, EWAUC is shown to be both effective and efficient.",
keywords = "Class imbalance learning, Online learning, Concept drift detection",
author = "Shuo Wang and Leandro Minku",
year = "2020",
month = sep,
day = "28",
doi = "10.1109/IJCNN48605.2020.9207377",
language = "English",
isbn = "978-1-7281-6927-9 (PoD)",
series = "Proceedings of International Joint Conference on Neural Networks",
publisher = "IEEE Computer Society Press",
booktitle = "Proceedings of the International Joint Conference on Neural Networks (IJCNN), World Congress on Computational Intelligence, 2020",
note = "IEEE International Joint Conference on Neural Networks (IJCNN), 2020 : World Congress on Computational Intelligence (WCCI 2020) ; Conference date: 19-07-2020 Through 24-07-2020",

}

RIS

TY - GEN

T1 - AUC estimation and concept drift detection for imbalanced data streams with multiple classes

AU - Wang, Shuo

AU - Minku, Leandro

PY - 2020/9/28

Y1 - 2020/9/28

N2 - Online class imbalance learning deals with data streams having very skewed class distributions. When learning from data streams, concept drift is one of the major challenges that deteriorate the classification performance. Although several approaches have been recently proposed to overcome concept drift in imbalanced data, they are all limited to two-class cases. Multi-class imbalance imposes additional challenges in concept drift detection and performance evaluation, such as a more severe imbalanced distribution and the limited choice of performance measures. This paper extends AUC for evaluating classifiers on multi-class imbalanced data in online learning scenarios. The proposed metrics, PMAUC, WAUC and EWAUC, are studied through comprehensive experiments, focusing on their characteristics on time-changing data streams and whether and how they can be used to detect concept drift. The AUC-based metrics show effectiveness in detecting concept drift in a variety of artificial data streams and a real-world data application with multiple classes. In particular, EWAUC is shown to be both effective and efficient.

AB - Online class imbalance learning deals with data streams having very skewed class distributions. When learning from data streams, concept drift is one of the major challenges that deteriorate the classification performance. Although several approaches have been recently proposed to overcome concept drift in imbalanced data, they are all limited to two-class cases. Multi-class imbalance imposes additional challenges in concept drift detection and performance evaluation, such as a more severe imbalanced distribution and the limited choice of performance measures. This paper extends AUC for evaluating classifiers on multi-class imbalanced data in online learning scenarios. The proposed metrics, PMAUC, WAUC and EWAUC, are studied through comprehensive experiments, focusing on their characteristics on time-changing data streams and whether and how they can be used to detect concept drift. The AUC-based metrics show effectiveness in detecting concept drift in a variety of artificial data streams and a real-world data application with multiple classes. In particular, EWAUC is shown to be both effective and efficient.

KW - Class imbalance learning

KW - Online learning

KW - Concept drift detection

U2 - 10.1109/IJCNN48605.2020.9207377

DO - 10.1109/IJCNN48605.2020.9207377

M3 - Conference contribution

SN - 978-1-7281-6927-9 (PoD)

T3 - Proceedings of International Joint Conference on Neural Networks

BT - Proceedings of the International Joint Conference on Neural Networks (IJCNN), World Congress on Computational Intelligence, 2020

PB - IEEE Computer Society Press

T2 - IEEE International Joint Conference on Neural Networks (IJCNN), 2020

Y2 - 19 July 2020 through 24 July 2020

ER -