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dc.contributor.authorTsai, Cheng-Jungen_US
dc.contributor.authorLee, Chien-I.en_US
dc.contributor.authorYang, Wei-Pangen_US
dc.date.accessioned2014-12-08T15:09:50Z-
dc.date.available2014-12-08T15:09:50Z-
dc.date.issued2009-03-01en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2007.11.034en_US
dc.identifier.urihttp://hdl.handle.net/11536/7537-
dc.description.abstractIn a database, the concept of all example might change along with time, which is known as concept drift. When the concept drift occurs, the classification model built by using the old dataset is not suitable for predicting a new dataset. Therefore, the problem of concept drift has attracted a lot of attention in recent years. Although many algorithms have been proposed to solve this problem, they have not been able to provide users with a satisfactory solution to concept drift. That is, the current research about concept drift focuses only on updating the classification model. However, real life decision makers might be very interested in the rules of concept drift. For example, doctors desire to know the root causes behind variation in the causes and development of disease. In this paper, we propose a concept drift rule mining tree, called CDR-Tree, to accurately discover the underlying rule governing concept drift. The main contributions of this paper are: (a) we address the problem of mining concept-drifting rules which has not been considered in previously developed classification schemes; (b) we develop a method that call accurately mine rules governing concept drift: (c) we develop a method that should classification models be required, call efficiently and accurately generate such models via a simple extraction procedure rather than constructing them anew; and (d) we propose two strategies to reduce the complexity of concept-drifting, rules mined by our CDR-Tree. (C) 2007 Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectData miningen_US
dc.subjectClassificationen_US
dc.subjectDecision treeen_US
dc.subjectData streamen_US
dc.subjectConcept driften_US
dc.titleMining decision rules on data streams in the presence of concept driftsen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.eswa.2007.11.034en_US
dc.identifier.journalEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.citation.volume36en_US
dc.citation.issue2en_US
dc.citation.spage1164en_US
dc.citation.epage1178en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000262178000021-
dc.citation.woscount11-
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