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 Citations (Scopus)

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
Volume6215 LNCS
DOIs
StatePublished - 2010
Externally publishedYes
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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Performance Comparison
Dimensionality Reduction
Reduction Method
Tumors
Tumor
Genes
Gene expression
Prediction
Scaling
Gene
Gene Selection
Gene Expression Profile
Curse of Dimensionality
Principal component analysis
Principal Component Analysis
Gene Expression
Learning systems
Machine Learning
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Filter

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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 (Vol. 6215 LNCS, 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

Performance comparison of tumor classification based on linear and non-linear dimensionality reduction methods. / Wang, Shu Lin; You, Hong Zhu; Lei, Ying Ke; Li, Xue Ling.

Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings. Vol. 6215 LNCS 2010. p. 291-300 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6215 LNCS).

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

Wang, SL, You, HZ, Lei, YK & Li, XL 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. vol. 6215 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6215 LNCS, pp. 291-300, 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, 8/18/10. https://doi.org/10.1007/978-3-642-14922-1_37
Wang SL, You HZ, Lei YK, Li XL. 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. Vol. 6215 LNCS. 2010. p. 291-300. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-14922-1_37
Wang, Shu Lin ; You, Hong Zhu ; Lei, Ying Ke ; Li, Xue Ling. / Performance comparison of tumor classification based on linear and non-linear dimensionality reduction methods. Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings. Vol. 6215 LNCS 2010. pp. 291-300 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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