Consider the canonical model of a classifier illustrated in Figure 1.9. The
degrees of support for a given input x can be interpreted in different ways, the two most common being confidences in the suggested labels and estimates of the posterior probabilities for the classes.
Let be a feature vector and Ω = {ω1, ω2, …, ωc} be the set of class labels. Each classifier Di in the ensemble outputs c degrees of support. Without loss of generality we can assume that all c degrees are in the interval [0, 1], that is, . Denote by di, j(x) the support that classifier Di gives to the hypothesis that x comes from class ωj. The larger the support, the more likely the class label ωj. The L classifier outputs for a particular input x can be organized in a decision profile (DP(x)) as the matrix shown in
Figure 5.1.
The methods described in this chapter use DP(x) to find the overall support for each class, and subsequently label the input x in the class with the largest support. There are two ...
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be a feature vector and Ω = {ω1, ω2, …, ωc} be the set of class labels. Each classifier Di in the ensemble 
outputs c degrees of support. Without loss of generality we can assume that all c degrees are in the interval [0, 1], that is, 
. Denote by di, j(x) the support that classifier Di gives to the hypothesis that x comes from class ωj. The larger the support, the more likely the class label ωj. The L classifier outputs for a particular input x can be organized in a decision profile (DP(x)) as the matrix shown in
Figure 5.1.