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Penalized Independent Component Discriminant Method for Tumor Classification

  • Chun-Hou Zheng
  • Li Shang
  • Yan Chen
  • Zhi-Kai Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)

Abstract

This paper proposes a new method for tumor classification using gene expression data. In this method, we first employ independent component analysis (ICA) to model the gene expression data, then apply optimal scoring algorithm to classify them. To show the validity of the proposed method, we apply it to classify two DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.

Keywords

Gene Expression Data Independent Component Analy Little Square Support Vector Machine Independent Component Analysis Principal Component Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chun-Hou Zheng
    • 1
  • Li Shang
    • 2
  • Yan Chen
    • 1
  • Zhi-Kai Huang
    • 2
  1. 1.School of Information and Communication TechnologyQufu Normal UniversityRizhaoChina
  2. 2.Department of AutomationUniversity of Science and Technology of ChinaHefeiChina

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