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請使用永久網址來引用或連結此文件:
http://ir.ncue.edu.tw/ir/handle/987654321/8859
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| 題名: | An Efficient and Sensitive Decision Tree Approach to Mining Concept-Drifting Data Streams |
| 作者: | Cheng-Jung Tsai;Chien-I. Lee;Wei-Pang Yang |
| 貢獻者: | 數學系 |
| 關鍵詞: | Data mining, Data streams, Incremental learning, Decision tree, Concept drif |
| 日期: | 2008-02
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| 上傳時間: | 2011-05-10T06:29:09Z
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| 出版者: | IOS Press |
| 摘要: | 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. |
| 關聯: | Informatica, 19(1):135-156 |
| 顯示於類別: | [數學系] 期刊論文
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文件中的檔案:
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| 2020102110002.pdf | 524Kb | Adobe PDF | 745 | 檢視/開啟 |
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