Learnability in rough set approaches

Abstract

We consider learning abilities of classifiers learned from data structured by rough set approaches into lower approximations of considered sets of objects. We introduce two measures, λ and δ, that estimate attainable predictive accuracy of rough-set-based classifiers. To check the usefulness of the estimates for various types of classifiers, we perform a computational experiment on fourteen data sets. In the experiment, we use two versions of the rough-set-based rule classifier, called VC-DomLEM, and few other well known classifiers. The results show that both introduced measures are useful for an a priori identification of data sets that are hard to learn by all classifiers. © 2010 Springer-Verlag Berlin Heidelberg.