In this paper, a framework for the analysis of the error-reject trade-off in linearly combined classifiers is proposed. We start from a framework developed by Tumer and Ghosh (1996, 1999). We extend this framework and analyse some hypotheses under which the linear combination of classifier outputs can improve the error-reject trade-off of the individual classifiers. Experiments that support some of the analytical results are reported.
['Fabio Roli', 'Giorgio Fumera', 'Gianni Vernazza']
Analysis of error-reject trade-off in linearly combined classifiers