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Showing papers on "Rough set published in 1986"


Journal ArticleDOI
TL;DR: The main objective of this paper is to show that the concept of “approximate classification” of a set is closely related to the statistical approach.
Abstract: Quinlan suggested an inductive algorithm based on the statistical theory of information originally proposed by Shannon. Recently Pawlak showed that the principles of inductive learning (learning from examples) can be precisely formulated on the basis of the theory of rough sets. These two approaches are apparently very different, although in both methods objects in the knowledge base are assumed to be characterized by “features” (attributes and attribute values). The main objective of this paper is to show that the concept of “approximate classification” of a set is closely related to the statistical approach. In fact, in the design of inductive programs, the criterion for selecting dominant attributes based on the concept of rough sets is a special case of the statistical method if equally probable distribution of objects in the “doubtful region” of the approximation space is assumed.

108 citations



Proceedings ArticleDOI
01 Dec 1986
TL;DR: It is demonstrated 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.
Abstract: 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.

26 citations