Hierarchical Cost-Sensitive Algorithms for Genome-Wide Gene Function Prediction
Nicolò Cesa-Bianchi, Giorgio Valentini;
JMLR W&CP 8:14-29, 2010.
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
, using the FunCat taxonomy and seven biomolecular
data sets, reveal a significant advantage of our techniques over "flat"
and cost-insensitive hierarchical ensembles.