Publication Type

Journal Article

Publication Date

10-2013

Abstract

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.

Keywords

Accuracy, Adaptation models, Classification, Data models, Detectors, Distributed databases, Predictive models, Training, concept drift, distributed systems

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

IEEE Transactions on Knowledge and Data Engineering (TKDE)

Volume

25

Issue

10

First Page

2343

Last Page

2355

ISSN

1041-4347

Identifier

10.1109/TKDE.2012.172

Publisher

IEEE

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1109/TKDE.2012.172

Share

COinS