NDLI: Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach

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Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach

Content Provider	Springer Nature Link
Author	Chen, Sheng He, Haibo
Copyright Year	2010
Abstract	Difficulties of learning from nonstationary data stream are generally twofold. First, dynamically structured learning framework is required to catch up with the evolution of unstable class concepts, i.e., concept drifts. Second, imbalanced class distribution over data stream demands a mechanism to intensify the underrepresented class concepts for improved overall performance. To alleviate the challenges brought by these issues, we propose the recursive ensemble approach (REA) in this paper. To battle against the imbalanced learning problem in training data chunk received at any timestamp t, i.e., $${{\mathcal{S}}_t,}$$ REA adaptively pushes into $${{\mathcal{S}}_t}$$ part of minority class examples received within [0, t − 1] to balance its skewed class distribution. Hypotheses are then progressively developed over time for all balanced training data chunks and combined together as an ensemble classifier in a dynamically weighted manner, which therefore addresses the concept drifts issue in time. Theoretical analysis proves that REA can provide less erroneous prediction results than a comparative algorithm. Besides that, empirical study on both synthetic benchmarks and real-world data set is also applied to validate effectiveness of REA as compared with other algorithms in terms of evaluation metrics consisting of overall prediction accuracy and ROC curve.
Starting Page	35
Ending Page	50
Page Count	16
File Format	PDF
ISSN	18686478
Journal	Evolving Systems
Volume Number	2
Issue Number	1
e-ISSN	18686486
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-11-19
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Incremental learning Nonstationary data Imbalanced learning Stream data Ensemble learning Concept drift Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) Complexity
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation Computer Science Applications

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