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...
| Published in: | BioMed Research International Vol. 2016; pp. 1 - 13 |
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| Main Authors: | , , , , |
| Format: | algorithm equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
8/4/2016
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=117191154&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117191154 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/4/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 117191154 117191154 117191154 10.1155/2016/9721713 117191154 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data. aug: au: Wang, Shuaiqun Aorigele Kong, Wei Zeng, Weiming Hong, Xiaomin affil: Information Engineering College, Shanghai Maritime University, Shanghai 201306, China sug: subj: Gene Expression Algorithms Data Analysis Neoplasms Classification Neoplasms Familial and Genetic Benchmarking Brain Neoplasms Familial and Genetic Neoplasms Diagnosis Microarray Analysis Descriptive Statistics Leukemia Familial and Genetic Lung Neoplasms Familial and Genetic Prostatic Neoplasms Familial and Genetic ab: 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. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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