Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data
Joint Authors
Zeng, Weiming
Wang, Shuaiqun
Aorigele, Shuaiqun
Hong, Xiaomin
Kong, Wei
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-04
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Shuaiqun& Aorigele, Shuaiqun& Kong, Wei& Zeng, Weiming& Hong, Xiaomin. 2016. Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data. BioMed Research International،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1099357
Modern Language Association (MLA)
Wang, Shuaiqun…[et al.]. Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data. BioMed Research International No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1099357
American Medical Association (AMA)
Wang, Shuaiqun& Aorigele, Shuaiqun& Kong, Wei& Zeng, Weiming& Hong, Xiaomin. Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1099357
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099357