基于对称不确定性和SVM递归特征消除的信息基因选择方法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201705005

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Abstract A large number of genes unrelated to tumor classification exist in gene expression profiles, and thus the prediction accuracy of tumor is reduced substantially. Due to the small sample size with high dimension and noise, the tumor diagnosis is harder. To get an informative gene subset with fewer genes and a better classification accuracy, an informative gene selection method based on symmetric uncertainty(SU) and support vector machine-recursive feature elimination(SVM-RFE) is proposed. Firstly, SU is used to evaluate the correlation between genes and class labels, and approximate Markov blanket is defined grounded on SU. The elimination of irrelevant and redundant genes is achieved. Secondly, SVM-RFE algorithm is applied to obtain the effective informative gene subset by further removing redundant genes. Experimental results show that the proposed algorithm produces higher classification performance with equal or less informative gene subset.

Key words： Gene Selection Symmetric Uncertainty Support Vector Machine Recursive Feature Elimination Tumor Classification

Received: 01 January 2017

ZTFLH:

TP 391

About author:: (YE Mingquan(Corresponding author), born in 1973, Ph.D., professor. His research interests include data mining, machine learning, biomedical informatics and big data of health care.)
(GAO Lingyun, born in 1991, master student. Her research interests include data mining, machine learning and biomedical informatics.)
(WU Changrong, born in 1973, master, associate professor. Her research interests include data mining, machine learning, biomedical image proce-ssing and analysis.)
(WAN Chunyuan, born in 1993, master student. Her research interests include data mining, machine learning, biomedical image processing and analysis.)

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	YE Mingquan
	GAO Lingyun
	WU Changrong
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Cite this article:

YE Mingquan,GAO Lingyun,WU Changrong等. Informative Gene Selection Method Based on Symmetric Uncertainty and SVM Recursive Feature Elimination[J]. , 2017, 30(5): 429-438.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201705005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I5/429

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