12. Identifying Potential Gene Markers Using SVM Classifier Ensemble

  1. Ujjwal Maulik3,
  2. Sanghamitra Bandyopadhyay4 and
  3. Jason T. L. Wang5
  1. Anirban Mukhopadhyay1,2,
  2. Ujjwal Maulik3 and
  3. Sanghamitra Bandyopadhyay4

Published Online: 25 AUG 2010

DOI: 10.1002/9780470872352.ch12

Computational Intelligence and Pattern Analysis in Biological Informatics

Computational Intelligence and Pattern Analysis in Biological Informatics

How to Cite

Mukhopadhyay, A., Maulik, U. and Bandyopadhyay, S. (2010) Identifying Potential Gene Markers Using SVM Classifier Ensemble, in Computational Intelligence and Pattern Analysis in Biological Informatics (eds U. Maulik, S. Bandyopadhyay and J. T. L. Wang), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470872352.ch12

Editor Information

  1. 3

    Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

  2. 4

    Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

  3. 5

    Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA

Author Information

  1. 1

    Department of Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany

  2. 2

    Department of Computer Science and Engineering, University of Kalyani, India

  3. 3

    Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

  4. 4

    Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

Publication History

  1. Published Online: 25 AUG 2010
  2. Published Print: 11 OCT 2010

ISBN Information

Print ISBN: 9780470581599

Online ISBN: 9780470872352

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Keywords:

  • identifying potential gene markers - using SVM classifier ensemble;
  • microarray gene expression data;
  • percentage classification accuracy - by kernel functions and their ensemble for all data sets

Summary

This chapter contains sections titled:

  • Introduction

  • Microarray Gene Expression Data

  • Support Vector Machine Classifier

  • Proposed Technique

  • Data Sets and Preprocessing

  • Experimental Results

  • Discussion and Conclusions

  • Acknowledgment

  • References