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

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...

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Main Authors: LI, Peipei, Hu, X., LIANG, Qianhui (Althea), GAO, Yunjun
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Language:English
Published: Institutional Knowledge at Singapore Management University 2009
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Online Access:https://ink.library.smu.edu.sg/sis_research/466
http://dx.doi.org/10.1007/978-3-642-03070-3_18
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-14652011-01-22T04:36:05Z Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees LI, Peipei Hu, X. LIANG, Qianhui (Althea) GAO, Yunjun 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. 2009-07-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/466 info:doi/10.1007/978-3-642-03070-3_18 http://dx.doi.org/10.1007/978-3-642-03070-3_18 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Data Streams - Ensemble Decision Trees - Concept Drift - Noise Computer Sciences
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Data Streams - Ensemble Decision Trees - Concept Drift - Noise
Computer Sciences
spellingShingle Data Streams - Ensemble Decision Trees - Concept Drift - Noise
Computer Sciences
LI, Peipei
Hu, X.
LIANG, Qianhui (Althea)
GAO, Yunjun
Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees
description 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.
format text
author LI, Peipei
Hu, X.
LIANG, Qianhui (Althea)
GAO, Yunjun
author_facet LI, Peipei
Hu, X.
LIANG, Qianhui (Althea)
GAO, Yunjun
author_sort LI, Peipei
title Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees
title_short Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees
title_full Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees
title_fullStr Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees
title_full_unstemmed Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees
title_sort concept drifting detection on noisy streaming data in random ensemble decision trees
publisher Institutional Knowledge at Singapore Management University
publishDate 2009
url https://ink.library.smu.edu.sg/sis_research/466
http://dx.doi.org/10.1007/978-3-642-03070-3_18
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