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Please use this identifier to cite or link to this item:
http://ir.ncue.edu.tw/ir/handle/987654321/8859
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| Title: | An Efficient and Sensitive Decision Tree Approach to Mining Concept-Drifting Data Streams |
| Authors: | Cheng-Jung Tsai;Chien-I. Lee;Wei-Pang Yang |
| Contributors: | 數學系 |
| Keywords: | Data mining, Data streams, Incremental learning, Decision tree, Concept drif |
| Date: | 2008-02
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| Issue Date: | 2011-05-10T06:29:09Z
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| Publisher: | IOS Press |
| Abstract: | Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data block is basically a random sample from a stationary distribution, but many databases available violate this assumption. That is, the class of an instance may change over time, known as concept drift. In this paper, we propose a Sensitive Concept Drift Probing Decision Tree algorithm (SCRIPT), which is based on the statistical X2 �test, to handle the concept drift problem on data streams. Compared with the proposed methods, the advantages of SCRIPT include: a) it can avoid unnecessary system cost for stable data streams; b) it can immediately and efficiently corrects original classifier while data streams are instable; c) it is more suitable to the applications in which a sensitive detection of concept drift is required. |
| Relation: | Informatica, 19(1):135-156 |
| Appears in Collections: | [數學系] 期刊論文
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Files in This Item:
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Size | Format | |
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| 2020102110002.pdf | 524Kb | Adobe PDF | 582 | View/Open |
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