Performance comparison of tumor classification based on linear and non-linear dimensionality reduction methods

Shu Lin Wang, Hong Zhu You, Ying Ke Lei, Xue Ling Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Gene expression profiles play more and more important roles in accurate tumor diagnosis and treatment. However, the curse of dimensionality that the number of genes far exceeds the number of samples issues the challenges to the traditional dimensionality reduction methods. Here based on two-stage dimensionality reduction model we design 18 tumor classification methods by combining two classical gene filters with three common dimensionality reduction methods: principal component analysis (PCA), linear discriminative analysis (LDA) and multidimensional scaling (MDS) method to extract discriminative features and use three common machine learning methods to evaluate the prediction accuracy of the extracted features on six tumor datasets, respectively. Although gene expression presents the non-linear characteristics, non-linear dimensionality reduction method MDS is not always the best in prediction accuracy among the three dimensionality reductions on all six tumor datasets. Moreover, the performance comparison indicates that no single dimensionality reduction is always superior to the others on all of the six tumor datasets. Our results also suggest that the prediction accuracy obtained depends strongly on the dataset, and less on the gene selection and classification methods.

Original languageEnglish (US)
Title of host publicationAdvanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings
Pages291-300
Number of pages10
DOIs
StatePublished - Oct 29 2010
Event6th International Conference on Intelligent Computing, ICIC 2010 - Changsha, China
Duration: Aug 18 2010Aug 21 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6215 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Intelligent Computing, ICIC 2010
CountryChina
CityChangsha
Period8/18/108/21/10

Keywords

  • Gene expression profiles
  • dimensionality reduction
  • linear discriminative analysis
  • multidimensional scaling
  • principal component analysis
  • tumor classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Wang, S. L., You, H. Z., Lei, Y. K., & Li, X. L. (2010). Performance comparison of tumor classification based on linear and non-linear dimensionality reduction methods. In Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings (pp. 291-300). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6215 LNCS). https://doi.org/10.1007/978-3-642-14922-1_37