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...
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Online Access: | http://eprints.usm.my/49389/ http://eprints.usm.my/49389/1/OSAMA%20AHMAD%20SULEIMAN%20ALOMARI_hj.pdf |
| _version_ | 1848881422832500736 |
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| author | Alomari, Osama Ahmad Suleiman |
| author_facet | Alomari, Osama Ahmad Suleiman |
| author_sort | Alomari, Osama Ahmad Suleiman |
| building | USM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T18:18:46Z |
| format | Thesis |
| id | usm-49389 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T18:18:46Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-493892021-07-05T01:04:28Z http://eprints.usm.my/49389/ Filter-Wrapper Methods For Gene Selection In Cancer Classification 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 NonPeerReviewed application/pdf en http://eprints.usm.my/49389/1/OSAMA%20AHMAD%20SULEIMAN%20ALOMARI_hj.pdf Alomari, Osama Ahmad Suleiman (2018) Filter-Wrapper Methods For Gene Selection In Cancer Classification. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | R856-857 Biomedical engineering. Electronics. Instrumentation Alomari, Osama Ahmad Suleiman Filter-Wrapper Methods For Gene Selection In Cancer Classification |
| title | 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_short | Filter-Wrapper Methods For Gene
Selection In Cancer Classification |
| title_sort | filter-wrapper methods for gene
selection in cancer classification |
| topic | R856-857 Biomedical engineering. Electronics. Instrumentation |
| url | http://eprints.usm.my/49389/ http://eprints.usm.my/49389/1/OSAMA%20AHMAD%20SULEIMAN%20ALOMARI_hj.pdf |