Abstract
We present a generalization of the original idea of rough sets as introduced by Pawlak. The generalization, called the Variable Precision Rough Sets Model with Asymmetric Bounds, is aimed at modeling decision situations characterized by uncertain information expressed in terms of probability distributions estimated form frequency distributions observed in empirical data. The model presented is a direct extension of the previous concept, the Variable Precision Rough Sets Model. The properties of the extended model are investigated and compared to the original model. Also, a real life problem of identifying the factors which most affect the likelihoods of specified events in the steel industry is discussed in the context of this theory.
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