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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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 |
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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 |
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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. |
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LI, Peipei Hu, X. LIANG, Qianhui (Althea) GAO, Yunjun |
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LI, Peipei Hu, X. LIANG, Qianhui (Althea) GAO, Yunjun |
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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 |
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Institutional Knowledge at Singapore Management University |
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2009 |
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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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