PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Hierarchical cost-sensitive algorithms for genome-wide gene function prediction
Nicolò Cesa-Bianchi and Giorgio Valentini
Journal of Machine Learning Research, W&C Proceedings Volume 8, pp. 14-29, 2010.

Abstract

In this work we propose new ensemble methods for the hierarchical classification of gene functions. Our methods exploit the hierarchical relationships between the classes in different ways: each ensemble node is trained “locally”, according to its position in the hierarchy; moreover, in the evaluation phase the set of predicted annotations is built so to minimize a global loss function defined over the hierarchy. We also address the problem of sparsity of annotations by introducing a cost-sensitive parameter that allows to control the precision-recall trade-off. Experiments with the model organism S. cerevisiae, using the FunCat taxonomy and seven biomolecular data sets, reveal a significant advantage of our techniques over “flat” and cost-insensitive hierarchical ensembles.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6301
Deposited By:Giorgio Valentini
Deposited On:08 March 2010