Region Classification with Decision Trees

The region-classification task is to construct class regions containing the correct classes of the objects being classified with a given probability. To turn a point classifier into a region classifier, the conformal framework is used . However, applying the framework requires a non-conformity function. This function estimates the instances´ non-conformity for the point classifier used. This paper studies how to turn decision trees into region classifiers. It considers two non-conformity functions. The first one is a general non-conformity function applicable to any point classifier . The second function is a specific non-conformity function for decision trees . Our main contribution is twofold. First we show, contrary to , that the general function outperforms the specific one for decision-tree region classifiers in terms of validity and efficiency of the class regions. Second, we show how the decision-tree complexity influences the quality of the class regions based on these two functions.