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Proceedings Paper

Representation and classification for high-throughput data
Author(s): Lodewyk F. A. Wessels; Marcel J. T. Reinders; Tibor van Welsem; Petra M. Nederlof
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Paper Abstract

Survival prediction and optimal treatment choice for cancer patients are dependent on correct disease classification. This classification can be improved significantly when high- throughput data such as microarray expression analysis is employed. These data sets usually suffer from the dimensionality problem: many features and few patients. Consequently, care must be taken when feature selection is performed and classifiers for disease classification are designed. In this paper we investigate several issues associated with this problem, including 1) data representation; 2) the type of classifier employed and 3) classifier construction, with specific emphasis on feature selection approaches. More specifically, 'filter' and 'wrapper' approaches for feature selection are studied. The different representations, selection criteria, classifiers and feature selection approaches are evaluated with regard to the effect on true classification performance. As test cases we employ a Comparative Genomic Hybridization breast cancer data sets and two publicly available gene expression data sets.

Paper Details

Date Published: 21 June 2002
PDF: 12 pages
Proc. SPIE 4626, Biomedical Nanotechnology Architectures and Applications, (21 June 2002); doi: 10.1117/12.472086
Show Author Affiliations
Lodewyk F. A. Wessels, Delft Univ. of Technology and The Netherlands Cancer Institute (Netherlands)
Marcel J. T. Reinders, Delft Univ. of Technology (Netherlands)
Tibor van Welsem, The Netherlands Cancer Institute (Netherlands)
Petra M. Nederlof, The Netherlands Cancer Institute (Netherlands)


Published in SPIE Proceedings Vol. 4626:
Biomedical Nanotechnology Architectures and Applications
Raymond P. Mariella; Catherine J. Murphy; Michelle Palmer; David A. Dunn; Darryl J. Bornhop; David A. Dunn; Raymond P. Mariella; Catherine J. Murphy; Dan V. Nicolau; Shuming Nie; Michelle Palmer; Ramesh Raghavachari; Darryl J. Bornhop; Ramesh Raghavachari; Shuming Nie; Ramesh Raghavachari, Editor(s)

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