We compare two ensemble methods to classify DNA microarray data. The methods use different strategies to face the course of dimensionality plaguing these data. One of them projects data along random coordinates, the other compresses them into independent boolean variables. Both result in random feature extraction procedures, feeding SVMs as base learners for a majority voting ensemble classifier. The classification capabilities are comparable, degrading on instances that are acknowledged anomalous in the literature.

BICA and Random Subspace ensembles for DNA microarray-based diagnosis / B. Apolloni, G. Valentini, A. Brega - In: Applied artificial intelligence : proceedings of the 7th nternational FLINS conference / [a cura di] D. Ruan [e altri]. - Singapore : World Scientific, 2006. - ISBN 9789812566904. - pp. 625-631 (( convegno International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics tenutosi a Genova, Italy nel 2006.

BICA and Random Subspace ensembles for DNA microarray-based diagnosis

B. Apolloni
Primo
;
G. Valentini
Secondo
;
2006

Abstract

We compare two ensemble methods to classify DNA microarray data. The methods use different strategies to face the course of dimensionality plaguing these data. One of them projects data along random coordinates, the other compresses them into independent boolean variables. Both result in random feature extraction procedures, feeding SVMs as base learners for a majority voting ensemble classifier. The classification capabilities are comparable, degrading on instances that are acknowledged anomalous in the literature.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/179223
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