A Multiscale Concept Drift Detection Method for Learning from Data Streams

Document Type

Conference Proceeding

Publication Date

12-4-2018

Abstract

Concept drifts can occur in data streams, which disable any models built on static data distribution to fit dynamic or recurrent concepts. If there is a feature that can monitor the stableness of such distribution, then we have a proper reference to adapt the model. Based on this idea, we propose a novel approach named Multiscale Drift Detection Test (MDDT) that localizes abrupt drift points when detection feature values fluctuate. MDDT is based on a resampling scheme and a paired student t-test. It applies a detection procedure on a broad and a narrow scale. This multiscale structure not only reduces massive time of a constant checking process, but also filters noise in the detection features. Experiments are performed via synthetic and real-world datasets. The results indicate that the proposed method outperforms the state-of-art algorithms in terms of computation cost and average accuracy.

Identifier

85059984425 (Scopus)

ISBN

[9781538635933]

Publication Title

IEEE International Conference on Automation Science and Engineering

External Full Text Location

https://doi.org/10.1109/COASE.2018.8560554

e-ISSN

21618089

ISSN

21618070

First Page

786

Last Page

790

Volume

2018-August

Grant

51775385

Fund Ref

National Natural Science Foundation of China

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