Feature selection algorithms for very high dimensional data and mixed data

Feature selection algorithms for very high dimensional data and mixed data

Feature selection is an important issue in pattern recognition. The goal of feature selection algorithm is to identify a set of relevant features, based on which to construct a classifier for a pattern recognition problem. This thesis addresses the problem of feature selection for very high dimensio...

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Main Author: Tang, Wen Yin
Other Authors: Mao Kezhi
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/10356/41404
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-414042023-07-04T16:53:37Z Feature selection algorithms for very high dimensional data and mixed data Tang, Wen Yin Mao Kezhi School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Feature selection is an important issue in pattern recognition. The goal of feature selection algorithm is to identify a set of relevant features, based on which to construct a classifier for a pattern recognition problem. This thesis addresses the problem of feature selection for very high dimensional data and mixed data, which exist in many application domains of pattern recognition nowadays. The proposed feature selection algorithms aim to eliminate both irrelevant and redundant features while retaining major discriminating underlying data. DOCTOR OF PHILOSOPHY (EEE) 2010-07-02T05:56:35Z 2010-07-02T05:56:35Z 2008 2008 Thesis Tang, W. Y. (2008). Feature selection algorithms for very high dimensional data and mixed data. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/41404 10.32657/10356/41404 en 188 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
Tang, Wen Yin
Feature selection algorithms for very high dimensional data and mixed data
description Feature selection is an important issue in pattern recognition. The goal of feature selection algorithm is to identify a set of relevant features, based on which to construct a classifier for a pattern recognition problem. This thesis addresses the problem of feature selection for very high dimensional data and mixed data, which exist in many application domains of pattern recognition nowadays. The proposed feature selection algorithms aim to eliminate both irrelevant and redundant features while retaining major discriminating underlying data.
author2 Mao Kezhi
author_facet Mao Kezhi
Tang, Wen Yin
format Theses and Dissertations
author Tang, Wen Yin
author_sort Tang, Wen Yin
title Feature selection algorithms for very high dimensional data and mixed data
title_short Feature selection algorithms for very high dimensional data and mixed data
title_full Feature selection algorithms for very high dimensional data and mixed data
title_fullStr Feature selection algorithms for very high dimensional data and mixed data
title_full_unstemmed Feature selection algorithms for very high dimensional data and mixed data
title_sort feature selection algorithms for very high dimensional data and mixed data
publishDate 2010
url https://hdl.handle.net/10356/41404
_version_ 1772828436434255872

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