We investigate the theoretical links between a regression ensemble and a linearly combined classification ensemble. First, we reformulate the Tumer & Ghosh model for linear combiners in a regression context; we then exploit this new formulation to generalise the concept of the “Ambiguity decomposition”, previously defined only for regression tasks, to classification problems. Finally, we propose a new algorithm, based on the Negative Correlation Learning framework, which applies to ensembles of linearly combined classifiers.

Ensemble Learning in Linearly Combined Classifiers via Negative Correlation / ZANDA M; BROWN G; FUMERA G; ROLI F. - LNCS 4472(2007), pp. 440-449. ((Intervento presentato al convegno 7th Int. Workshop on Multiple Classifier Systems (MCS 2007) tenutosi a Prague, Czech Republic nel May 23-25 2007.

Ensemble Learning in Linearly Combined Classifiers via Negative Correlation

FUMERA, GIORGIO;ROLI, FABIO
2007

Abstract

We investigate the theoretical links between a regression ensemble and a linearly combined classification ensemble. First, we reformulate the Tumer & Ghosh model for linear combiners in a regression context; we then exploit this new formulation to generalise the concept of the “Ambiguity decomposition”, previously defined only for regression tasks, to classification problems. Finally, we propose a new algorithm, based on the Negative Correlation Learning framework, which applies to ensembles of linearly combined classifiers.
978-3-540-72481-0
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/25815
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