Rough Set Theory Measures to Knowledge Generation

The accelerated growth of the information volumes on processes, phenomena and reports brings about an increasing interest in the possibility of discovering knowledge from data sets. This is a challenging task because in many cases it deals with extremely large, inherently not structured and fuzzy data, plus the presence of uncertainty. Therefore it is required to know a priori the quality of future procedures without using any additional information. In this paper we propose new measures to evaluate the quality of training sets used by algorithms for learning of supervised classifiers. Our training set assessment relied on measures furnished by rough sets theory. Our experimental results involved three classifiers (k-NN, C-4.5 and MLP) from international data bases. New training sets are built taking into account the results of the measures and the accuracy obtained by the classifiers, with the aim of infer the accuracy that the classifiers would obtain using a new training set. This is possible using a rule generator (C4.5) and a function estimation algorithm (k-NN).