Filter-Wrapper Methods For Gene Selection In Cancer Classification

In microarray gene expression studies, finding the smallest subset of informative genes from microarray datasets for clinical diagnosis and accurate cancer classification is one of the most difficult challenges in machine learning task. Many researchers have devoted their efforts to address this...

Full description

Saved in:

Bibliographic Details
Main Author:	Alomari, Osama Ahmad Suleiman
Format:	Thesis
Language:	English
Published:	2018
Subjects:	R856-857 Biomedical engineering Electronics Instrumentation
Online Access:	http://eprints.usm.my/49389/1/OSAMA%20AHMAD%20SULEIMAN%20ALOMARI_hj.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-usm-ep.49389
record_format	uketd_dc
spelling	my-usm-ep.493892021-07-05T01:04:28Z Filter-Wrapper Methods For Gene Selection In Cancer Classification 2018-09 Alomari, Osama Ahmad Suleiman R856-857 Biomedical engineering. Electronics. Instrumentation In microarray gene expression studies, finding the smallest subset of informative genes from microarray datasets for clinical diagnosis and accurate cancer classification is one of the most difficult challenges in machine learning task. Many researchers have devoted their efforts to address this problem by using a filter method, a wrapper method or a combination of both approaches. A hybrid method is a hybridisation approach between filter and wrapper methods. It benefits from the speed of the filter approach and the accuracy of the wrapper approach. Several hybrid filter-wrapper methods have been proposed to select informative genes. However, hybrid methods encounter a number of limitations, which are associated with filter and wrapper approaches. The gene subset that is produced by filter approaches lacks predictiveness and robustness. The wrapper approach encounters problems of complex interactions among genes and stagnation in local optima. To address these drawbacks, this study investigates filter and wrapper methods to develop effective hybrid methods for gene selection. This study proposes new hybrid filter-wrapper methods based on Maximum Relevancy Minimum Redundancy (MRMR) as a filter approach and adapted bat-inspired algorithm (BA) as a wrapper approach. First, MRMR hybridisation and BA adaptation are investigated to resolve the gene selection problem. The proposed method is called MRMR-BA. 2018-09 Thesis http://eprints.usm.my/49389/ http://eprints.usm.my/49389/1/OSAMA%20AHMAD%20SULEIMAN%20ALOMARI_hj.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	R856-857 Biomedical engineering Electronics Instrumentation
spellingShingle	R856-857 Biomedical engineering Electronics Instrumentation Alomari, Osama Ahmad Suleiman Filter-Wrapper Methods For Gene Selection In Cancer Classification
description	In microarray gene expression studies, finding the smallest subset of informative genes from microarray datasets for clinical diagnosis and accurate cancer classification is one of the most difficult challenges in machine learning task. Many researchers have devoted their efforts to address this problem by using a filter method, a wrapper method or a combination of both approaches. A hybrid method is a hybridisation approach between filter and wrapper methods. It benefits from the speed of the filter approach and the accuracy of the wrapper approach. Several hybrid filter-wrapper methods have been proposed to select informative genes. However, hybrid methods encounter a number of limitations, which are associated with filter and wrapper approaches. The gene subset that is produced by filter approaches lacks predictiveness and robustness. The wrapper approach encounters problems of complex interactions among genes and stagnation in local optima. To address these drawbacks, this study investigates filter and wrapper methods to develop effective hybrid methods for gene selection. This study proposes new hybrid filter-wrapper methods based on Maximum Relevancy Minimum Redundancy (MRMR) as a filter approach and adapted bat-inspired algorithm (BA) as a wrapper approach. First, MRMR hybridisation and BA adaptation are investigated to resolve the gene selection problem. The proposed method is called MRMR-BA.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Alomari, Osama Ahmad Suleiman
author_facet	Alomari, Osama Ahmad Suleiman
author_sort	Alomari, Osama Ahmad Suleiman
title	Filter-Wrapper Methods For Gene Selection In Cancer Classification
title_short	Filter-Wrapper Methods For Gene Selection In Cancer Classification
title_full	Filter-Wrapper Methods For Gene Selection In Cancer Classification
title_fullStr	Filter-Wrapper Methods For Gene Selection In Cancer Classification
title_full_unstemmed	Filter-Wrapper Methods For Gene Selection In Cancer Classification
title_sort	filter-wrapper methods for gene selection in cancer classification
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2018
url	http://eprints.usm.my/49389/1/OSAMA%20AHMAD%20SULEIMAN%20ALOMARI_hj.pdf
_version_	1747821995437850624

Filter-Wrapper Methods For Gene Selection In Cancer Classification

Similar Items