2008Unpublished venueRequires access

Rough set theory and its application in the intelligent systems

Wei Pan, Jinhui Yi, Ye San

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Abstract

The rough set theory is a new mathematical tool to study vague and uncertain information, and is widely used in intelligent systems. In this paper, the basic ideas of rough set theory are introduced, and the notion of up and low approximation sets, attribute reduction, core and some extensions of rough set theory are also presented. Then the application of rough set theory in intelligent systems. The combination of rough sets with fuzzy sets, neural network and genetic algorithms is mainly reviewed, which gives new ideas and methods to solve the hard problems in intelligent control.

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What this paper is about

The rough set theory is a new mathematical tool to study vague and uncertain information, and is widely used in intelligent systems. In this paper, the basic ideas of rough set theory are introduced, and the notion of up and low approximation sets, attribute reduction, core and some extensions of rough set theory are also presented. Then the application of rough set theory in intelligent systems. The combination of rough sets with fuzzy sets, neural network and genetic algorithms is mainly reviewed, which gives new ideas and methods to solve the hard problems in intelligent control.

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OpenAlex reports 10 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The rough set theory is a new mathematical tool to study vague and uncertain information, and is widely used in intelligent systems. In this paper, the basic ideas of rough set theory are introduced, and the notion of up and low approximation sets, attribute reduction, core and some extensions of rough set theory are also presented. Then the application of rough set theory in intelligent systems. The combination of rough sets with fuzzy sets, neural network and genetic algorithms is mainly reviewed, which gives new ideas and methods to solve the hard problems in intelligent control.

Key concepts: Rough set, Fuzzy set, Set theory, Computer science, Dominance-based rough set approach, Set (abstract data type), Intelligent decision support system, Reduction (mathematics)

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