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Type: Journal article
Title: Learning accurate very fast decision trees from uncertain data streams
Author: Liang, C.
Zhang, Y.
Shi, P.
Hu, Z.
Citation: International Journal of Systems Science, 2015; 46(16):3032-3050
Publisher: Taylor & Francis
Issue Date: 2015
ISSN: 0020-7721
Statement of
Chunquan Liang, Yang Zhang, Peng Shi and Zhengguo Hu
Abstract: Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing an uncertain VFDT tree with classifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty.
Keywords: Uncertain data streams; very fast decision tree; functional tree leaf; uncertain numerical attributes
Rights: © 2014 Taylor & Francis
RMID: 0030031037
DOI: 10.1080/00207721.2014.895877
Appears in Collections:Electrical and Electronic Engineering publications

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