Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/77898
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Type: Journal article
Title: Learning very fast decision tree from uncertain data streams with positive and unlabeled samples
Author: Liang, C.
Zhang, Y.
Shi, P.
Hu, Z.
Citation: Information Sciences, 2012; 213:50-60
Publisher: Elsevier Science Inc
Issue Date: 2012
ISSN: 0020-0255
1872-6291
Statement of
Responsibility: 
Chunquan Liang, Yang Zhang, Peng Shi, Zhengguo Hu
Abstract: Most data stream classification algorithms need to supply input with a large amount of precisely labeled data. However, in many data stream applications, streaming data contains inherent uncertainty, and labeled samples are difficult to be collected, while abundant data are unlabeled. In this paper, we focus on classifying uncertain data streams with only positive and unlabeled samples available. Based on concept-adapting very fast decision tree (CVFDT) algorithm, we propose an algorithm namely puuCVFDT (CVFDT for positive and unlabeled uncertain data). Experimental results on both synthetic and real-life datasets demonstrate the strong ability and efficiency of puuCVFDT to handle concept drift with uncertainty under positive and unlabeled learning scenario. Even when 90% of the samples in the stream are unlabeled, the classification performance of the proposed algorithm is still compared to that of CVFDT, which is learned from fully labeled data without uncertainty. © 2012 Elsevier Inc. All rights reserved.
Keywords: Uncertain data stream
Very fast decision tree
Positive unlabeled learning
Uncertain attribute
Rights: © 2012 Elsevier Inc. All rights reserved.
DOI: 10.1016/j.ins.2012.05.023
Published version: http://dx.doi.org/10.1016/j.ins.2012.05.023
Appears in Collections:Aurora harvest
Electrical and Electronic Engineering publications

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