Predictive Handling of Asynchronous Concept Drifts in Distributed Environments
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. The authors address this problem by developing an ensemble approach, PINE (Predictive and parameter INsensitive Ensemble), that combines reactive adaptation via drift detection, and proactive handling of upcoming changes via early warning and adaptation across the peers.