Microarray Data Analysis with Support Vector Machine

  • Si-Hao Du
  • Jin-Tsong Jeng
  • Shun-Feng Su
  • Sheng-Chieh Chang
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 345)


Microarray data analysis approach has became a widely used tool for disease detection. It uses tens of thousands of genes as input dimension that would be a huge computational problem for data analysis. In this chapter, the proposed approach deals with selection of feature genes and classification of microarray data under support vector machine (SVM) approach. Feature genes can be finding out according to the adjustable epsilon-support vector regression (epsilon-SVR) and then to select high ranked genes after all microarray data. Moreover, multi-class support vector classification (multi-class SVC) and cross-validation methods apply to acquire great prediction classification accuracy and less computing time.


Support vector machine Support vector regression Multi-class support vector classification Feature genes Microarray data analysis 



The authors wish to thank that this work was supported by National Science Council Under Grant NSC 95-2221-E-150-085, NSC 101-2221-E-150-048-MY2.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Si-Hao Du
    • 1
  • Jin-Tsong Jeng
    • 2
  • Shun-Feng Su
    • 1
  • Sheng-Chieh Chang
    • 3
  1. 1.Department of Electrical EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational Formosa UniversityYunlin CountyTaiwan
  3. 3.Aeronautical Systems Research DivisionNational Chung-Shan Institute of Science and TechnologyTaichungTaiwan

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