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Cancer classification by minimizing fuzzy scattering effect
Bioinf. Applic. Res. Centre, James Cook Univ., Townsville, QLD .ORCID iD: 0000-0002-4255-5130
2008 (English)Conference paper (Refereed)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.2008.4630394ISBN: 978-1-4244-1819-0ISBN: 978-1-4244-1818-3OAI: oai:DiVA.org:liu-125019DiVA: diva2:902777
Conference
IEEE International Conference on Fuzzy Systems, 2008. FUZZ-IEEE 2008.(IEEE World Congress on Computational Intelligence).1-6 June 2008. Hong Kong
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2016-02-23

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

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ReferencesLink to record
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