A model driven approach to imbalanced data learning

Ph.D

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Bibliographic Details
Main Author:	YIN HONGLI
Other Authors:	COMPUTER SCIENCE
Format:	Theses and Dissertations
Language:	English
Published:	2012
Subjects:	Imbalanced Data, Model Driven Sampling, Data Sampling, Domain Knowledge, Context Sensitive, Asthma Control
Online Access:	http://scholarbank.nus.edu.sg/handle/10635/30689
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Institution:	National University of Singapore
Language:	English

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spelling	sg-nus-scholar.10635-306892017-10-21T08:04:07Z A model driven approach to imbalanced data learning YIN HONGLI COMPUTER SCIENCE LEONG TZE YUN Imbalanced Data, Model Driven Sampling, Data Sampling, Domain Knowledge, Context Sensitive, Asthma Control Ph.D DOCTOR OF PHILOSOPHY 2012-02-29T18:00:29Z 2012-02-29T18:00:29Z 2011-03-15 Thesis YIN HONGLI (2011-03-15). A model driven approach to imbalanced data learning. ScholarBank@NUS Repository. http://scholarbank.nus.edu.sg/handle/10635/30689 NOT_IN_WOS en
institution	National University of Singapore
building	NUS Library
country	Singapore
collection	ScholarBank@NUS
language	English
topic	Imbalanced Data, Model Driven Sampling, Data Sampling, Domain Knowledge, Context Sensitive, Asthma Control
spellingShingle	Imbalanced Data, Model Driven Sampling, Data Sampling, Domain Knowledge, Context Sensitive, Asthma Control YIN HONGLI A model driven approach to imbalanced data learning
description	Ph.D
author2	COMPUTER SCIENCE
author_facet	COMPUTER SCIENCE YIN HONGLI
format	Theses and Dissertations
author	YIN HONGLI
author_sort	YIN HONGLI
title	A model driven approach to imbalanced data learning
title_short	A model driven approach to imbalanced data learning
title_full	A model driven approach to imbalanced data learning
title_fullStr	A model driven approach to imbalanced data learning
title_full_unstemmed	A model driven approach to imbalanced data learning
title_sort	model driven approach to imbalanced data learning
publishDate	2012
url	http://scholarbank.nus.edu.sg/handle/10635/30689
_version_	1681080975054864384

A model driven approach to imbalanced data learning

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