Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees

Publication Type

Conference Proceeding Article

Publication Date

7-2009

Abstract

Although a vast majority of inductive learning algorithms has been developed for handling of the concept drifting data streams, especially the ones in virtue of ensemble classification models, few of them could adapt to the detection on the different types of concept drifts from noisy streaming data in a light demand on overheads of time and space. Motivated by this, a new classification algorithm for Concept drifting Detection based on an ensembling model of Random Decision Trees (called CDRDT) is proposed in this paper. Extensive studies with synthetic and real streaming data demonstrate that in comparison to several representative classification algorithms for concept drifting data streams, CDRDT not only could effectively and efficiently detect the potential concept changes in the noisy data streams, but also performs much better on the abilities of runtime and space with an improvement in predictive accuracy. Thus, our proposed algorithm provides a significant reference to the classification for concept drifting data streams with noise in a light weight way.

Keywords

Data Streams - Ensemble Decision Trees - Concept Drift - Noise

Discipline

Computer Sciences

Publication

MLDM´2009

ISBN

9783642030697

Identifier

10.1007/978-3-642-03070-3_18

Publisher

Springer Verlag

Additional URL

http://dx.doi.org/10.1007/978-3-642-03070-3_18

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