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Many solutions to cost-sensitive classification (and regression) rely on someor all of the following assumptions: we have complete knowledge about the costcontext at training time, we can easily re-train whenever the cost contextchanges, and we have technique-specific methods (such as cost-sensitivedecision trees) that can take advantage of that information. In this paper weaddress the problem of selecting models and minimising joint cost (integratingboth misclassification cost and test costs) without any of the aboveassumptions. We introduce methods and plots (such as the so-called JROC plots)that can work with any off-the-shelf predictive technique, including ensembles,such that we reframe the model to use the appropriate subset of attributes (thefeature configuration) during deployment time. In other words, models aretrained with the available attributes (once and for all) and then deployed bysetting missing values on the attributes that are deemed ineffective forreducing the joint cost. As the number of feature configuration combinationsgrows exponentially with the number of features we introduce quadratic methodsthat are able to approximate the optimal configuration and model choices, asshown by the experimental results.

Test cost and misclassification cost trade-off using reframing