Ensemble learning in linearly combined classifiers via negative correlation

Abstract

We investigate the theoretical links between a regression ensemble and a linearly combined classification ensemble. First, we reformulate the Turner & 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. © Springer-Verlag Berlin Heidelberg 2007.