On learning and evaluation of decision rules in the context of rough sets

We demonstrate in this paper that the principles of inductive learning can be precisely formulated and hopefully better understood based on the theory of rough sets introduced by Pawlak. We discuss some statistical aspects of evaluating and forming decision rules from examples of expert decisions. We also suggest a method of comparing decision rules inferred by different learning algorithms from the same set of samples.

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