PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

BICA and Random Subspace ensembles for DNA microarray-based diagnosis
B. Apolloni, G. Valentini and A. Brega
In: Applied Artificial Intelligence - Proc. of the 7th International FLINS Conference (2006) World Scientific .

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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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:2363
Deposited By:Giorgio Valentini
Deposited On:22 November 2006