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CLC number: TP391
On-line Access: 2011-08-03
Received: 2010-09-25
Revision Accepted: 2011-03-09
Crosschecked: 2011-07-04
Cited: 4
Clicked: 7735
Clustering feature decision trees for semi-supervised classification from high-speed data streams
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Wen-hua Xu, Zheng Qin, Yang Chang. Clustering feature decision trees for semi-supervised classification from high-speed data streams[J]. Journal of Zhejiang University Science C, 2011, 12(8): 615-628.
@article{title="Clustering feature decision trees for semi-supervised classification from high-speed data streams",
author="Wen-hua Xu, Zheng Qin, Yang Chang",
journal="Journal of Zhejiang University Science C",
volume="12",
number="8",
pages="615-628",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1000330"
}
%0 Journal Article
%T Clustering feature decision trees for semi-supervised classification from high-speed data streams
%A Wen-hua Xu
%A Zheng Qin
%A Yang Chang
%J Journal of Zhejiang University SCIENCE C
%V 12
%N 8
%P 615-628
%@ 1869-1951
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1000330
TY - JOUR
T1 - Clustering feature decision trees for semi-supervised classification from high-speed data streams
A1 - Wen-hua Xu
A1 - Zheng Qin
A1 - Yang Chang
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 8
SP - 615
EP - 628
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1000330
Abstract: Most stream data classification algorithms apply the supervised learning strategy which requires massive labeled data. Such approaches are impractical since labeled data are usually hard to obtain in reality. In this paper, we build a clustering feature decision tree model, CFDT, from data streams having both unlabeled and a small number of labeled examples. CFDT applies a micro-clustering algorithm that scans the data only once to provide the statistical summaries of the data for incremental decision tree induction. Micro-clusters also serve as classifiers in tree leaves to improve classification accuracy and reinforce the any-time property. Our experiments on synthetic and real-world datasets show that CFDT is highly scalable for data streams while generating high classification accuracy with high speed.
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