Model of Covering Rough Sets in the Machine Intelligence
Rough set theory has been proposed by Pawlak as a useful tool for dealing with the vagueness and granularity in information systems. Classical rough set theory is based on equivalence relation. The covering rough sets are an improvement of Pawlak rough set to deal with complex practical problems which the latter one can not handle. This paper studies covering-based generalized rough sets. In this setting, we investigate common properties of classical lower and upper approximation operations hold for the covering-based lower and upper approximation operations and relationships among some type of covering rough sets.
H. M. Nie and J. Q. Zhou, "Model of Covering Rough Sets in the Machine Intelligence", Advanced Materials Research, Vol. 548, pp. 735-739, 2012