Informative Gene Selection Method Based on Symmetric Uncertainty and SVM Recursive Feature Elimination
YE Mingquan1, GAO Lingyun1, WU Changrong2, WAN Chunyuan1
1.Department of Computer Science, Wannan Medical College, Wuhu 241002 2.School of Mathematics and Computer Science, Anhui Normal University, Wuhu 241003
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.
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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2017, Vol. 30 
