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Cancer classification by minimizing fuzzy scattering effect
Bioinformatics Applications Research Centre; and the School of Mathematics, Physics and Information Technology, James Cook University, Australia.ORCID iD: 0000-0002-4255-5130
2008 (English)Conference paper, Published paper (Refereed)
Resource type
Text
Abstract [en]

Proteomic technology has been found promising for classifying complex diseases that leads to early prediction. However, for effective classification, the extraction of good features that can represent the identities of different classes plays the frontal critical factor for any classification problems. In addition, another major problem associated with pattern recognition is how to effectively handle a large feature space. This paper addresses these two frontal issues for mass spectrometry (MS) classification. We apply the theory of linear predictive coding to extract features and fuzzy vector quantization to reduce the large feature space of MS data. The minimization of the fuzzy scattering matrix in the setting of the fuzzy c-means algorithm provides better grouping for feature classification. The proposed methodology was tested using two MS-based cancer datasets and the results are promising.

Place, publisher, year, edition, pages
2008. 377-380 p.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-125019DOI: 10.1109/FUZZY.2008.4630394ISI: 000262974000061Scopus ID: 2-s2.0-55249100784ISBN: 978-1-4244-1819-0 (print)ISBN: 978-1-4244-1818-3 (print)OAI: oai:DiVA.org:liu-125019DiVA: diva2:902777
Conference
IEEE International Conference on Fuzzy Systems, 2008. FUZZ-IEEE 2008.(IEEE World Congress on Computational Intelligence). Hong Kong, 1-6 June 2008
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2017-06-30Bibliographically approved

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Pham, Tuan D
Computer Vision and Robotics (Autonomous Systems)

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Total: 259 hits
CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
Language
  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
  • text
  • asciidoc
  • rtf