Learnability in rough set approaches

Authors:
Jerzy Błaszczyński;Roman Słowiński;Marcin Szeląg
Affiliations:
Institute of Computing Science, Poznań University of Technology, Poznań, Poland;Institute of Computing Science, Poznań University of Technology, Poznań, Poland and Institute for Systems Research, Polish Academy of Sciences, Warsaw, Poland;Institute of Computing Science, Poznań University of Technology, Poznań, Poland
Venue:
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Year:
2010

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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.