Resumen

FILIBERTO, Yaima; BELLO, Rafael; CABALLERO, Yailé  y  FRIAS, Mabel. ALGORITHM TO LEARN CLASIFICATION RULES BASED ON THE EXTENDED ROUGH SET THEORY. Dyna rev.fac.nac.minas [online]. 2011, vol.78, n.169, pp.62-70. ISSN 0012-7353.

Rough sets have allowed developing several machine learning techniques, among them methods to discover rules of classification. In this paper, we present an algorithm to generate rules of classification based on similarity relations, this allows to apply this method in the case of features with discrete or real domains. The experimental results show a satisfactory performance of this algorithm in comparison with other such as C4.5 and MODLEM

Palabras clave : Classification rules; similarity relations; Rough set theory.

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