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21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07)
Mining Concept Drifts from Data Streams Based on Multi-Classifiers
Niagara Falls, Ontario, Canada
May 21-May 23
ISBN: 0-7695-2847-3
ASCII Text
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Yue Sun, Guojun Mao, Xu Liu, Chunnian Liu, "Mining Concept Drifts from Data Streams Based on Multi-Classifiers," Advanced Information Networking and Applications Workshops, International Conference on, vol. 2, pp. 257-263, 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07), 2007.
 
 
BibTex
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@article{ 10.1109/AINAW.2007.250,
author = {Yue Sun and Guojun Mao and Xu Liu and Chunnian Liu},
title = {Mining Concept Drifts from Data Streams Based on Multi-Classifiers},
journal ={Advanced Information Networking and Applications Workshops, International Conference on},
volume = {2},
year = {2007},
isbn = {0-7695-2847-3},
pages = {257-263},
doi = {http://doi.ieeecomputersociety.org/10.1109/AINAW.2007.250},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Advanced Information Networking and Applications Workshops, International Conference on
TI - Mining Concept Drifts from Data Streams Based on Multi-Classifiers
SN - 0-7695-2847-3
SP257
EP263
A1 - Yue Sun,
A1 - Guojun Mao,
A1 - Xu Liu,
A1 - Chunnian Liu,
PY - 2007
KW - null
VL - 2
JA - Advanced Information Networking and Applications Workshops, International Conference on
ER -
 
Yue Sun, Beijing University of Technology, China
Guojun Mao, Beijing University of Technology, China
Xu Liu, Beijing University of Technology, China
Chunnian Liu, Beijing University of Technology, China
Mining concept drifts is one of the most important fields in mining data streams. In this paper, a new ensemble algorithm called ICEA is proposed for mining concept drifts from data streams, which uses ensemble multi-classifiers to detect concept changes from the data streams in an incremental way. The experimental results show that ICEA algorithm performs higher accuracy and better adaptability than the popular methods such as SEA algorithm.
Citation:
Yue Sun, Guojun Mao, Xu Liu, Chunnian Liu, "Mining Concept Drifts from Data Streams Based on Multi-Classifiers," ainaw, vol. 2, pp.257-263, 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07), 2007
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