Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 10
2986-2991

Gene expression data classification method based on FCBF feature selection and ensemble optimized learning

Ma Chao
School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen Guangdong 518172, China
DOI: 10.19734/j.issn.1001-3695.2018.04.0248

Abstract

In order to solve the problems of microarray gene expression data with the characteristic of high dimension and small sample, high redundancy and a lot of noise, this paper proposed a novel model FICS-EKELM, which was built based on the combination FCBF feature selection and ensemble optimized method, for gene expression data classification. In the proposed method, it firstly used fast correlation-based filter method(FCBF) to eliminate the irrelevant features and noise, and chose the discriminate feature subsets. Secondly, bootstrap technology produced many sample training subsets, by means of these subsets, it used the improved crow search algorithm(ICS) to select optimal feature subsets and optimized parameters for kernel extreme learning machine(KELM) synchronously. And then, it constructed ensemble classifiers for target gene data classification, which based on the basic classifiers. Moreover, the model used parallel method on multi-core platform multithreading processor, which used OpenMP, to speed up the search and optimization process. Experiment on six public famous gene datasets shows that the proposed method not only achieves a higher classification performance with less characteristic genes, but also greatly improves the classification accuracy. It proves the validity of the proposed method.

Foundation Support

国家自然科学基金青年基金资助项目(61303113)
广东省自然科学基金资助项目(2016A030310072)
深圳市科技计划项目(KJYY20170724152553858)
广东省教育厅重点平台及科研项目特色创新类项目(2017GWTSCX040)
深圳市2017年度规划课题(ybzz17011,ybzz17009,zdzz17005)
校级科研课题(QN201716)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.04.0248
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 10
Section: Algorithm Research & Explore
Pages: 2986-2991
Serial Number: 1001-3695(2019)10-024-2986-06

Publish History

[2019-10-05] Printed Article

Cite This Article

马超. 基于FCBF特征选择和集成优化学习的基因表达数据分类算法 [J]. 计算机应用研究, 2019, 36 (10): 2986-2991. (Ma Chao. Gene expression data classification method based on FCBF feature selection and ensemble optimized learning [J]. Application Research of Computers, 2019, 36 (10): 2986-2991. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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