Machine learning: rough sets perspective

The paper presents a non-inductive, incremental technique for learning from examples derived within the context of the probabilistic variable Precision Rough Sets model. The technique involves the classification of the domain of interest into a relatively small number of categories followed by computation of all, or some, minimal rules with probabilities by using the concept of a decision matrix