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Predictive Handling of Asynchronous Concept Drifts in Distributed Environments
Oct. 2013 (vol. 25 no. 10)
pp. 2343-2355
Hock Hee Ang, Nanyang Technological University, Singapore
Vivekanand Gopalkrishnan, Deloitte Analytics Institute Asia, Singapore
Indre Zliobaite, Bournemouth University, Poole
Mykola Pechenizkiy, Eindhoven University of Technology, The Netherlands
Steven C.H. Hoi, Nanyang Technological University, Singapore
In a distributed computing environment, peers collaboratively learn to classify concepts of interest from each other. When external changes happen and their concepts drift, the peers should adapt to avoid increase in misclassification errors. The problem of adaptation becomes more difficult when the changes are asynchronous, i.e., when peers experience drifts at different times. We address this problem by developing an ensemble approach, PINE, that combines reactive adaptation via drift detection, and proactive handling of upcoming changes via early warning and adaptation across the peers. With empirical study on simulated and real-world data sets, we show that PINE handles asynchronous concept drifts better and faster than current state-of-the-art approaches, which have been designed to work in less challenging environments. In addition, PINE is parameter insensitive and incurs less communication cost while achieving better accuracy.
Index Terms:
Data models,Predictive models,Accuracy,Distributed databases,Adaptation models,Detectors,Training,distributed systems,Data models,Predictive models,Accuracy,Distributed databases,Adaptation models,Detectors,Training,concept drift,Classification
Citation:
Hock Hee Ang, Vivekanand Gopalkrishnan, Indre Zliobaite, Mykola Pechenizkiy, Steven C.H. Hoi, "Predictive Handling of Asynchronous Concept Drifts in Distributed Environments," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 10, pp. 2343-2355, Oct. 2013, doi:10.1109/TKDE.2012.172
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