English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6469/11641
Visitors : 15350518      Online Users : 487
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/8859

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
Issue Date: 2011-05-10T06:29:09Z
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

Files in This Item:

File SizeFormat
2020102110002.pdf524KbAdobe PDF582View/Open


All items in NCUEIR are protected by copyright, with all rights reserved.

 


DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback