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Countering the Concept-drift Problem in Big Data Using iOVFDT
Hang Yang; Simon Fong
2013-10-28
Conference Name2013 IEEE International Congress on Big Data
Source PublicationProceedings - 2013 IEEE International Congress on Big Data, BigData 2013
Pages126-132
Conference Date27 June-2 July 2013
Conference PlaceSanta Clara, CA, USA
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

How to efficiently uncover the knowledge hidden within massive and big data remains an open problem. One of the challenges is the issue of 'concept drift' in streaming data flows. Concept drift is a well-known problem in data analytics, in which the statistical properties of the attributes and their target classes shift over time, making the trained model less accurate. Many methods have been proposed for data mining in batch mode. Stream mining represents a new generation of data mining techniques, in which the model is updated in one pass whenever new data arrive. This one-pass mechanism is inherently adaptive and hence potentially more robust than its predecessors in handling concept drift in data streams. In this paper, we evaluate the performance of a family of decision-tree-based data stream mining algorithms. The advantage of incremental decision tree learning is the set of rules that can be extracted from the induced model. The extracted rules, in the form of predicate logics, can be used subsequently in many decision-support applications. However, the induced decision tree must be both accurate and compact, even in the presence of concept drift. We compare the performance of three typical incremental decision tree algorithms (VFDT [2], ADWIN [3], iOVFDT [4]) in dealing with concept-drift data. Both synthetic and real-world drift data are used in the experiment. iOVFDT is found to produce superior results. 

KeywordClassification Concept Drift Data Stream Mining Incremental Decision Tree
DOIhttps://doi.org/10.1109/BigData.Congress.2013.25
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000332528300017
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Cited Times [WOS]:10   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of Computer and Information Science, University of Macau, Macau SAR, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Hang Yang,Simon Fong. Countering the Concept-drift Problem in Big Data Using iOVFDT[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2013:126-132.
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