Mining minimal discriminatice features sets and its applications to gene expression data analysis

Feature subset selection has been an important problem in machine learning research. Recently, new appeared data with high dimensionality, such as microarray gene expression data and text classification data, drive feature subset selection techniques advance speedily.

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Bibliographic Details
Main Author:	Feng, Chu
Other Authors:	Wang Lipo
Format:	Theses and Dissertations
Language:	English
Published:	2010
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Online Access:	https://hdl.handle.net/10356/40532
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-405322023-07-04T16:53:03Z Mining minimal discriminatice features sets and its applications to gene expression data analysis Feng, Chu Wang Lipo School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Feature subset selection has been an important problem in machine learning research. Recently, new appeared data with high dimensionality, such as microarray gene expression data and text classification data, drive feature subset selection techniques advance speedily. DOCTOR OF PHILOSOPHY (EEE) 2010-06-16T04:52:25Z 2010-06-16T04:52:25Z 2008 2008 Thesis Feng, C. (2008). Mining minimal discriminatice features sets and its applications to gene expression data analysis. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/40532 10.32657/10356/40532 en 170 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Feng, Chu Mining minimal discriminatice features sets and its applications to gene expression data analysis
description	Feature subset selection has been an important problem in machine learning research. Recently, new appeared data with high dimensionality, such as microarray gene expression data and text classification data, drive feature subset selection techniques advance speedily.
author2	Wang Lipo
author_facet	Wang Lipo Feng, Chu
format	Theses and Dissertations
author	Feng, Chu
author_sort	Feng, Chu
title	Mining minimal discriminatice features sets and its applications to gene expression data analysis
title_short	Mining minimal discriminatice features sets and its applications to gene expression data analysis
title_full	Mining minimal discriminatice features sets and its applications to gene expression data analysis
title_fullStr	Mining minimal discriminatice features sets and its applications to gene expression data analysis
title_full_unstemmed	Mining minimal discriminatice features sets and its applications to gene expression data analysis
title_sort	mining minimal discriminatice features sets and its applications to gene expression data analysis
publishDate	2010
url	https://hdl.handle.net/10356/40532
_version_	1772826578145771520

Mining minimal discriminatice features sets and its applications to gene expression data analysis

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