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 [1,2]. 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 th...

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 propos...

Part production requires constant monitoring to assure the effective manufacturing of high-quality components. The choice of monitoring methods can become a crucial factor in the decisions made during and prior to manufacturing. In an ideal world, designers and manufacturers will work together to interpret manufacturing and part data to assure the elimination of faults in manufacturing. However...