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 \u201clocally\u201d, 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 7 biomolecular data sets, reveal a significant advantage of
our techniques over \u201cflat\u201d and cost-insensitive hierarchical ensembles
