A Filter Feature Selection Method Based on MFA Score and Redundancy Excluding and It’s Application to Tumor Gene Expression Data Analysis

  • Jiangeng Li
  • Lei SuEmail author
  • Zenan Pang
Original Research Article


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.


Filter feature selection MFA score+ Redundant features Tumor gene expression data 



Marginal Fisher analysis


Fisher discriminant analysis


Support vector machine

MFA score+

Marginal Fisher analysis score and redundancy excluding



This work is supported by the Project for the National Key Technology R&D Program under Grant No. 2011BAC12B0304 and the Scientific Plan of Beijing Municipal Commission of Education under Grant No. JC002011200903.


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Copyright information

© International Association of Scientists in the Interdisciplinary Areas and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Institute of Artificial Intelligence and Robotics, College of Electronic Information & Control EngineeringBeijing University of TechnologyBeijingChina
  2. 2.Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijingChina

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