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Efficient Classification of Concept Drift in Data Stream
https://meral.edu.mm/records/6640
https://meral.edu.mm/records/6640742fcec3-d760-4a23-b0dc-7eec6af2e209
23c50a96-bf9e-4854-b81d-fbd1016142f0
Name / File | License | Actions |
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Efficient Classification of Concept Drift in Data Stream.pdf (165 Kb)
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Publication type | ||||||
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Conference paper | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Efficient Classification of Concept Drift in Data Stream | |||||
Language | en | |||||
Publication date | 2017-11-02 | |||||
Authors | ||||||
Ei Thwe Khaing | ||||||
Description | ||||||
The classification in data streams is widely studied in the literature over the last decade. In recent literature, many research contributions use incremental or progressive learning strategies to classify the data streams. Stream classification is a variant of incremental learning of classifiers that has to satisfy requirements specific for massive streams of data. There are many methods such as single classifiers, windowing techniques, drift detectors and ensemble methods. The classifier ensembles provide a way of adapting to changes by modifying ensemble components or their aggregation method. Adaptive Classifier Ensemble (ACE) method use to provide a natural way of adapting to change by modifying ensemble members. This method improves more accuracy and adaptable than other ensemble methods. |
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Keywords | ||||||
Data Stream Mining, Classification, Concept drift | ||||||
Conference papers | ||||||
ICAIT-2017 | ||||||
1-2 November, 2017 | ||||||
1st International Conference on Advanced Information Technologies | ||||||
Yangon, Myanmar | ||||||
Workshop Session | ||||||
https://www.uit.edu.mm/icait-2017/ |