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

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

Colleges, School and Institutes

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.

Details

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks (IJCNN), World Congress on Computational Intelligence, 2020
Publication statusPublished - 28 Sep 2020
EventIEEE International Joint Conference on Neural Networks (IJCNN), 2020 : World Congress on Computational Intelligence (WCCI 2020) - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameProceedings of International Joint Conference on Neural Networks
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407

Conference

ConferenceIEEE International Joint Conference on Neural Networks (IJCNN), 2020
CountryUnited Kingdom
CityGlasgow
Period19/07/2024/07/20

Keywords

  • Class imbalance learning, Online learning, Concept drift detection