Predictive handling of asynchronous concept drifts in distributed environments

H.H. Ang, V. Gopalkrishnan, I. Zliobaite, M. Pechenizkiy, S.C.H. Hoi

Research output: Contribution to journalArticleAcademicpeer-review

24 Citations (Scopus)
2 Downloads (Pure)


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 datasets, 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. Keywords: Classi¿cation, Distributed Systems, Concept Drift.
Original languageEnglish
Pages (from-to)2343-2365
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number10
Publication statusPublished - 2013


Dive into the research topics of 'Predictive handling of asynchronous concept drifts in distributed environments'. Together they form a unique fingerprint.

Cite this