Proceedings ArticleDOI
Concept drift detection for online class imbalance learning
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The analysis reveals that detecting drift in imbalanced data streams is a more difficult task than in balanced ones, and proposes a new detection method DDM-OCI derived from the existing methodDDM.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.read more
Citations
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SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
TL;DR: The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data because of its simplicity in the design, as well as its robustness when applied to different type of problems.
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Characterizing concept drift
TL;DR: This work presents the first comprehensive framework for quantitative analysis of drift, giving rise to a new comprehensive taxonomy of concept drift types and a solid foundation for research into mechanisms to detect and address concept drift.
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Resampling-Based Ensemble Methods for Online Class Imbalance Learning
TL;DR: This paper gives the first comprehensive analysis of class imbalance in data streams, in terms of data distributions, imbalance rates and changes in class imbalance status, and proposes two new ensemble methods that maintain both OOB and UOB with adaptive weights for final predictions, called WEOB1 and WEOb2.
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A Systematic Study of Online Class Imbalance Learning with Concept Drift
TL;DR: This paper first provides a comprehensive review of current research progress in this field, including current research focuses and open challenges, and an in-depth experimental study is performed, with the goal of understanding how to best overcome concept drift in online learning with class imbalance.
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Journal ArticleDOI
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Haibo He,E.A. Garcia +1 more
TL;DR: A critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario is provided.
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Proceedings ArticleDOI
Mining concept-drifting data streams using ensemble classifiers
TL;DR: This paper proposes a general framework for mining concept-drifting data streams using weighted ensemble classifiers, and shows that the proposed methods have substantial advantage over single-classifier approaches in prediction accuracy, and the ensemble framework is effective for a variety of classification models.
Proceedings Article
Learning from Time-Changing Data with Adaptive Windowing
Albert Bifet,Ricard Gavaldà +1 more
TL;DR: A new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time is presented, using sliding windows whose size is recomputed online according to the rate of change observed from the data in the window itself.