Deriving biased classifiers for better ROC performance

ROC analysis makes it possible to evaluate how well classifiers will perform given certain misclassification costs and class distributions. Given a set of classifiers, it also provides a method for constructing a hybrid classifier that optimally uses the available classifiers. Now in some cases it is possible to derive multiple classifiers from a single one, in a cheap way, and such that these classifiers focus on different areas of the ROC diagram, such that a hybrid classifier with better overall ROC performance can be constructed. This principle is quite generally applicable; here we describe a method to apply it to decision tree classifiers. An experimental evaluation illustrates the usefulness of the technique.status: publishe