Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/108537
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Type: Conference paper
Title: A selectively re-train approach based on clustering to classify concept-drifting data streams with skewed distribution
Author: Zhang, D.
Shen, H.
Hui, T.
Li, Y.
Wu, J.
Sang, Y.
Citation: Lecture Notes in Artificial Intelligence, 2014 / Tseng, V., Ho, T., Zhou, Z., Chen, A., Kao, H. (ed./s), vol.8444 LNAI, iss.PART 2, pp.413-424
Publisher: Springer
Issue Date: 2014
ISBN: 9783319066042
ISSN: 0302-9743
1611-3349
Conference Name: 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) (13 May 2014 - 16 May 2014 : Tainan, Taiwan)
Editor: Tseng, V.
Ho, T.
Zhou, Z.
Chen, A.
Kao, H.
Statement of
Responsibility: 
Dandan Zhang, Hong Shen, Tian Hui, Yidong Li, Jun Wu, and Yingpeng Sang
Abstract: Classification is an important and practical tool which uses a model built on historical data to predict class labels for new arrival data. In the last few years, there have been many interesting studies on classification in data streams. However, most such studies assume that those data streams are relatively balanced and stable. Actually, skewed data streams (e.g., few positive but lots of negatives) are very important and typical, which appear in many real world applications. Concept drifts and skewed distributions, two common properties of data streams, make the task of learning in streams particularly difficult and the traditional data mining algorithms no longer work. In this paper, we propose a method (Selectively Re-train Approach Based on Clustering) which can deal with concept-drifting and skewed distribution simultaneously. We evaluate our algorithm on both synthetic and real data sets simulating skewed data streams. Empirical results show the proposed method yields better performance than the previous work.
Keywords: Data stream; skewed distribution; concept-drifting; selectively Re-train.
Description: LNCS, volume 8444
Rights: © Springer International Publishing Switzerland 2014
DOI: 10.1007/978-3-319-06605-9_34
Appears in Collections:Aurora harvest 3
Computer Science publications

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