title: A Filter Feature Selection Method Based on MFA Score and Redundancy Excluding and It's Application to Tumor Gene Expression Data Analysis.
abstract: Feature selection techniques have been widely applied to tumor gene expression data analysis in recent years. A filter feature selection method named marginal Fisher analysis score (MFA score) which is based on graph embedding has been proposed, and it has been widely used mainly because it is superior to Fisher score. Considering the heavy redundancy in gene expression data, we proposed a new filter feature selection technique in this paper. It is named MFA score+ and is based on MFA score and redundancy excluding. We applied it to an artificial dataset and eight tumor gene expression datasets to select important features and then used support vector machine as the classifier to classify the samples. Compared with MFA score, t test and Fisher score, it achieved higher classification accuracy.
authors: Institute of Artificial Intelligence and Robotics, College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing, 100124, China.|Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China.
authors: Jiangeng
authors: J
authors: Li
authors: Institute of Artificial Intelligence and Robotics, College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing, 100124, China. sulei@emails.bjut.edu.cn.|Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China. sulei@emails.bjut.edu.cn.
authors: Lei
authors: L
authors: Su
authors: Institute of Artificial Intelligence and Robotics, College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing, 100124, China.
authors: Zenan
authors: Z
authors: Pang
mesh_terms: D000465:Algorithms; D000818:Animals; D030541:Databases, Genetic; D006801:Humans; D060388:Support Vector Machine
keywords: Filter feature selection; MFA score+; Redundant features; Tumor gene expression data