A Novel Method Based on Ant Colony Optimization for Gene Selection

Guang Di Cui; Gang Wang; Ying Li; Ji Zhang Fan

doi:10.4028/www.scientific.net/AMR.834-836.1850

Paper Titles

Design on Virtual Machine Frame of Cloud Computing Platform
p.1832

The Weakly Chain Transitive Maps
p.1836

The Research of Group Hierarchy Access Control in E-Government System
p.1840

Research on Standard Domain and Non Standard Domain Based on SIP Protocol
p.1844

A Novel Method Based on Ant Colony Optimization for Gene Selection
p.1850

The Application and Development of the Internet of Things in Intelligent Buildings
p.1854

Studying on the Industrial Competitiveness of Internet of Things in China's Provinces Based on Principal Component Analysis
p.1858

Research the Influence of Entrepreneurs' Cognition on Dynamic Capability Based on Resources Restriction
p.1862

On the Dynamical Properties of the Sun-Earth-Moon Restricted Four Body Problem
p.1869

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 834-836A Novel Method Based on Ant Colony Optimization...

A Novel Method Based on Ant Colony Optimization for Gene Selection

Abstract:

In this paper, an ant colony optimization based method (AM) is proposed for gene selection. AM consists of two stages. In the first stage, some redundant genes are filtered by information gain (IG). In the second stage, a fuzzy adaptive ant colony optimization is applied to gene selection. We evaluate the performance of AM on five gene expression datasets, which have dimensions varying from 7129 to 12000. We also compare the performance of AM with the results obtained from four existing well-known optimization algorithms. The comparison details show that AM could get better classification accuracy.

You might also be interested in these eBooks

Research in Materials and Manufacturing Technologies

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 834-836)

Pages:

1850-1853

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.834-836.1850

Citation:

Cite this paper

Online since:

October 2013

Authors:

Guang Di Cui, Gang Wang*, Ying Li, Ji Zhang Fan

Keywords:

Ant Colony Optimization (ACO), Feature Selection, Gene Selection, High-Dimensional Data

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Y. Sun, S. Todorovic, S. Goodison, Local-Learning-Based Feature Selection for High-Dimensional Data Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32(2010), 1610-1626.

DOI: 10.1109/tpami.2009.190

Google Scholar

[2] Y. C. Liu, C. P. Cheng, V. S. Tseng, Discovering relational-based association rules with multiple minimum supports on microarray datasets, Bioinformatics. 27(2011)3142-3148.

DOI: 10.1093/bioinformatics/btr526

Google Scholar

[3] L. J. Ke, Z. R. Feng, Z. G. Ren, An efficient ant colony optimization approach to attribute reduction in rough set theory, Pattern Recognition Letters. 29(2008)1351-1357.

DOI: 10.1016/j.patrec.2008.02.006

Google Scholar

[4] H. Yu , G. Gu, H. Li, J. Shen, J. Zhao, A Modified Ant Colony Optimization Algorithm for Tumor Marker Gene Selection, Genomics Proteomics & Bioinformatics. 7(2009)200-208.

DOI: 10.1016/s1672-0229(08)60050-9

Google Scholar

[5] Y. Li, G. Wang, H. Chen, L. Shi, L. Qin, An ant colony optimization based dimension reduction method for high-dimensional datasets, Journal of Bionic Engineering. 10(2013)231-241.

DOI: 10.1016/s1672-6529(13)60219-x

Google Scholar

[6] S. Baccianella, A. Esuli, F. Sebastiani, Using micro-documents for feature selection: The case of ordinal text classification. Expert Systems with Applications. 40(2013)4687-4696.

DOI: 10.1016/j.eswa.2013.02.010

Google Scholar