Cargando…

Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection u...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Shuaiqun, Aorigele, Kong, Wei, Zeng, Weiming, Hong, Xiaomin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4989135/
https://www.ncbi.nlm.nih.gov/pubmed/27579323
http://dx.doi.org/10.1155/2016/9721713
_version_ 1782448521136308224
author Wang, Shuaiqun
Aorigele,
Kong, Wei
Zeng, Weiming
Hong, Xiaomin
author_facet Wang, Shuaiqun
Aorigele,
Kong, Wei
Zeng, Weiming
Hong, Xiaomin
author_sort Wang, Shuaiqun
collection PubMed
description Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes.
format Online
Article
Text
id pubmed-4989135
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Hindawi Publishing Corporation
record_format MEDLINE/PubMed
spelling pubmed-49891352016-08-30 Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data Wang, Shuaiqun Aorigele, Kong, Wei Zeng, Weiming Hong, Xiaomin Biomed Res Int Research Article Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes. Hindawi Publishing Corporation 2016 2016-08-04 /pmc/articles/PMC4989135/ /pubmed/27579323 http://dx.doi.org/10.1155/2016/9721713 Text en Copyright © 2016 Shuaiqun Wang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Shuaiqun
Aorigele,
Kong, Wei
Zeng, Weiming
Hong, Xiaomin
Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data
title Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data
title_full Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data
title_fullStr Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data
title_full_unstemmed Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data
title_short Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data
title_sort hybrid binary imperialist competition algorithm and tabu search approach for feature selection using gene expression data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4989135/
https://www.ncbi.nlm.nih.gov/pubmed/27579323
http://dx.doi.org/10.1155/2016/9721713
work_keys_str_mv AT wangshuaiqun hybridbinaryimperialistcompetitionalgorithmandtabusearchapproachforfeatureselectionusinggeneexpressiondata
AT aorigele hybridbinaryimperialistcompetitionalgorithmandtabusearchapproachforfeatureselectionusinggeneexpressiondata
AT kongwei hybridbinaryimperialistcompetitionalgorithmandtabusearchapproachforfeatureselectionusinggeneexpressiondata
AT zengweiming hybridbinaryimperialistcompetitionalgorithmandtabusearchapproachforfeatureselectionusinggeneexpressiondata
AT hongxiaomin hybridbinaryimperialistcompetitionalgorithmandtabusearchapproachforfeatureselectionusinggeneexpressiondata