Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach

High dimension data are often associated with redundant features and there exist many information-theoretic approaches used to select the most relevant set of features and to reduce the feature size. The three most significant approaches are filter, wrap- per, and embedded approaches. Most filter ap...

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主要作者:	Jothi, Neesha
格式:	Thesis
語言:	English
出版:	2020
主題:	QA75.5-76.95 Electronic computers Computer science
在線閱讀:	http://eprints.usm.my/52445/1/Pages%20from%202.%20Final%20Thesis%20Submission.pdf
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id	my-usm-ep.52445
record_format	uketd_dc
spelling	my-usm-ep.524452022-04-28T08:39:09Z Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach 2020-11 Jothi, Neesha QA75.5-76.95 Electronic computers. Computer science High dimension data are often associated with redundant features and there exist many information-theoretic approaches used to select the most relevant set of features and to reduce the feature size. The three most significant approaches are filter, wrap- per, and embedded approaches. Most filter approaches fail to identify the individual contribution of every feature in each set of features in achieving an optimal feature subset. While the wrapper approaches encounter problems from complex interactions among features and stagnation in local optima. To address, these drawbacks, this study investigates filter and wrapper approaches to develop effective hybrid approaches for feature selection. 2020-11 Thesis http://eprints.usm.my/52445/ http://eprints.usm.my/52445/1/Pages%20from%202.%20Final%20Thesis%20Submission.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Jothi, Neesha Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach
description	High dimension data are often associated with redundant features and there exist many information-theoretic approaches used to select the most relevant set of features and to reduce the feature size. The three most significant approaches are filter, wrap- per, and embedded approaches. Most filter approaches fail to identify the individual contribution of every feature in each set of features in achieving an optimal feature subset. While the wrapper approaches encounter problems from complex interactions among features and stagnation in local optima. To address, these drawbacks, this study investigates filter and wrapper approaches to develop effective hybrid approaches for feature selection.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Jothi, Neesha
author_facet	Jothi, Neesha
author_sort	Jothi, Neesha
title	Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach
title_short	Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach
title_full	Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach
title_fullStr	Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach
title_full_unstemmed	Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach
title_sort	feature selection method based on hybrid filter-metaheuristic wrapper approach
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2020
url	http://eprints.usm.my/52445/1/Pages%20from%202.%20Final%20Thesis%20Submission.pdf
_version_	1747822181467815936

Feature Selection Method Based On Hybrid Filter-Metaheuristic Wrapper Approach

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