NDLI: Concept drift detection for online class imbalance learning

Content Provider	IEEE Xplore Digital Library
Author	Shuo Wang Minku, L.L. Ghezzi, D. Caltabiano, D. Tino, P. Xin Yao
Copyright Year	2013
Description	Author affiliation: STMicroelectron. SRL, Agrate Brianza, Italy (Ghezzi, D.; Caltabiano, D.) \|\| Sch. of Comput. Sci., Univ. of Birmingham, Birmingham, UK (Shuo Wang; Minku, L.L.; Tino, P.; Xin Yao)
Abstract	Concept drift detection methods are crucial components of many online learning approaches. Accurate drift detections allow prompt reaction to drifts and help to maintain high performance of online models over time. Although many methods have been proposed, no attention has been given to data streams with imbalanced class distributions, which commonly exist in real-world applications, such as fault diagnosis of control systems and intrusion detection in computer networks. This paper studies the concept drift problem for online class imbalance learning. We look into the impact of concept drift on single-class performance of online models based on three types of classifiers, under seven different scenarios with the presence of class imbalance. The analysis reveals that detecting drift in imbalanced data streams is a more difficult task than in balanced ones. Minority-class recall suffers from a significant drop after the drift involving the minority class. Overall accuracy is not suitable for drift detection. Based on the findings, we propose a new detection method DDM-OCI derived from the existing method DDM. DDM-OCI monitors minority-class recall online to capture the drift. The results show a quick response of the online model working with DDM-OCI to the new concept.
Starting Page	1
Ending Page	10
File Size	532431
Page Count	10
File Format	PDF
ISSN	21614407
e-ISBN	9781467361293
DOI	10.1109/IJCNN.2013.6706768
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Detectors Decision trees Neural networks Monitoring Bagging Computational modeling
Content Type	Text
Resource Type	Article

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