Approximate Reducts in Decision Tables

We develop the idea of approximate determinism in the decision prediction. Our approach is based on generalization of well known methods for analyzing frequencies in view of inference in decision tables. Some new kinds of reducts of information are proposed which seem to be more suitable for nding decision rules than those deened by crisp indiscernibility. We present them in view of preferences of the decision maker, data decomposition and algorithmic framework.