Probabilistic Reframing for Cost-Sensitive Regression

Cost-Sensitive Reachability Heuristics for Probabilistic Planning

Reachability heuristics have lead to impressive scale-ups in deterministic planning making their application to probabilistic planning a promising research direction. We describe how one such reachability heuristic (based on planning graphs) can be extended to handle a class of cost-sensitive probabilistic planning problems. Specifically, we address the problem of conformant (non-observable) pr...

One-sided Support Vector Regression for Multiclass Cost-sensitive Classification

We propose a novel approach that reduces cost-sensitive classification to one-sided regression. The approach stores the cost information in the regression labels and encodes the minimum-cost prediction with the onesided loss. The simple approach is accompanied by a solid theoretical guarantee of error transformation, and can be used to cast any one-sided regression method as a costsensitive cla...

An extended tuning method for cost-sensitive regression and forecasting

Test cost and misclassification cost trade-off using reframing

Many solutions to cost-sensitive classification (and regression) rely on some or all of the following assumptions: we have complete knowledge about the cost context at training time, we can easily re-train whenever the cost context changes, and we have technique-specific methods (such as cost-sensitive decision trees) that can take advantage of that information. In this paper we address the pro...

A graphical analysis of cost-sensitive regression problems

Several efforts have been done to bring ROC analysis beyond (binary) classification, especially in regression. However, the mapping and possibilities of these proposals do not correspond to what we expect from the analysis of operating conditions, dominance, hybrid methods, etc. In this paper we present a new representation of regression models in the so-called regression ROC (RROC) space. The ...