Gene selection in a gene decision space with application to gene expression data classification

被引:0
作者
Wang, Yuxian [1 ]
Li, Zhaowen [2 ]
Zhang, Jie [1 ]
Yu, Guangji [3 ]
机构
[1] Guangdong Songshan Polytech, Sch Comp & Informat Engn, Shaoguan, Guangdong, Peoples R China
[2] Yulin Normal Univ, Key Lab Complex Syst Optimizat & Big Data Proc, Dept Guangxi Educ, Yulin, Guangxi, Peoples R China
[3] Guangxi Univ Finance & Econ, Sch Big Data & Artificial Intelligence, Nanning, Guangxi, Peoples R China
关键词
Gene expression data; Gene decision space; Gene selection; Uncertainty measurement; UNCERTAINTY MEASURES; INFORMATION-SYSTEM; ROUGH SETS; ENTROPY; REDUCTION; ALGORITHM;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Gene selection is an important research topic in data mining. A gene decision space means a real-valued decision information system (RVDIS) where objects, conditional attributes and information values are cells, genes and gene expression values, respectively. This paper explores gene selection in a gene decision space based on information entropy and considers its application for gene expression data classification. In the first place, the distance between two cells in a given decision subspace is constructed. In the next place, the binary relations induced by this decision subspace are defined. After that, some information entropy for a gene decision space are investigated. Lastly, several gene selection algorithms in a gene decision space are presented by using the presented information entropy. The presented algorithms are applied to gene expression data classifications. Multiple publicly available gene expression datasets are employed to evaluate the gene selection performances of the proposed algorithms, while two commonly-used classifiers, KNN and CART, are utilized to obtain 10 fold cross validation accuracy of classification (ACC). The classification results demonstrated that the proposed algorithms can lower significantly the number genes selected, achieve the higher ACC, and outperform the other competing methods, such as raw data, Fisher, tSNE, PCA, FMIFRFS and DNEAR, with respect to gene number and ACC.
引用
收藏
页码:5021 / 5044
页数:24
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