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Incremental Learning Algorithms for Fast Classification in Data Stream
Simon Fong1; Zhicong Luo1; Bee Wah Yap2
2013
Conference Name2013 International Symposium on Computational and Business Intelligence
Source PublicationProceedings - 2013 International Symposium on Computational and Business Intelligence, ISCBI 2013
Pages186-190
Conference Date24-26 Aug. 2013
Conference PlaceNew Delhi, India
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

Classification is one of the most commonly used data mining methods which can make a prediction by modeling from the known data. However, in traditional classification, we need to acquire the whole dataset and then build a training model which may take a lot of time and resource consumption. Another drawback of the traditional classification is that it cannot process the dataset timely and efficiently, especially for real-time data stream or big data. In this paper, we evaluate a lightweight method based on incremental learning algorithms for fast classification. We use this method to do outlier detection via several popular incremental learning algorithms, like Decision Table, Naïve Bayes, J48, VFI, KStar, etc. 

KeywordClassification Data Mining Incremental Learning Lightweight Processing Oulier Dectction
DOIhttps://doi.org/10.1109/ISCBI.2013.45
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS IDWOS:000350145100039
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Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computer and Information Science, University of Macau, Macau SAR
2.Faculty Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Simon Fong,Zhicong Luo,Bee Wah Yap. Incremental Learning Algorithms for Fast Classification in Data Stream[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2013:186-190.
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