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...

Full description

Saved in:
Bibliographic Details
Main Author: Jothi, Neesha
Format: Thesis
Language:English
Published: 2020
Subjects:
Online Access:http://eprints.usm.my/52445/1/Pages%20from%202.%20Final%20Thesis%20Submission.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
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