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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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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 |
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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 |
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