Feature selection algorithms using Rough Set Theory

Rough Sets Theory has opened new trends for the development of the Incomplete Information Theory. Inside this one, the notion of reduct is a very significant one, but to obtain a reduct in a decision system is an expensive computing process although very important in data analysis and knowledge discovery. Because of this, it has been necessary the development of different variants to calculate reducts. The present work look into the utility that offers Rough Sets Model and Information Theory in feature selection and three methods are presented with the purpose of calculate good reducts. The first algorithm is MRSReduct, a variant of the method RSReduct; both methods consist of a greedy algorithm that uses heuristics to work out good reducts in acceptable times. In this paper we propose other method to find good reducts: RSRed*; this method combines several elements of Rough Set Theory. The new methods are compared with others which are implemented inside Pattern Recognition, Genetic Algorithm and Ant Colony Optimization Algorithms and the results of the statistical tests are shown.