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Feature set enhancement via hierarchical clustering for microarray classification

Author
Bosio, M.; Bellot, P.; Salembier, P.; Oliveras, A.
Type of activity
Presentation of work at congresses
Name of edition
GENSIPS 2011 - IEEE International Workshop on Genomic Signal Processing and Statistics
Date of publication
2011
Presentation's date
2011
Book of congress proceedings
Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
First page
226
Last page
229
Repository
http://hdl.handle.net/2117/87114 Open in new window
Abstract
A new method for gene expression classification is proposed in this paper. In a first step, the original feature set is enriched by including new features, called metagenes, produced via hierarchical clustering. In a second step, a reliable classifier is built from a wrapper feature selection process. The selection relies on two criteria: the classical classification error rate and a new reliability measure. As a result, a classifier with good predictive ability using as few features as possible...
Citation
Bosio, M., Bellot, P., Salembier, P., Oliveras, A. Feature set enhancement via hierarchical clustering for microarray classification. A: IEEE International Workshop on Genomic Signal Processing and Statistics. "Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics". 2011, p. 226-229.
Keywords
Cancer microarray classification, Feature selection, Hierarchical clustering, Treelet
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants