Probabilistic approaches to rough sets | Zendy

Yao Y. Y. | Zendy

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Probabilistic approaches to rough sets

Author(s) -

Yao Y. Y.

Publication year - 2003

Publication title -

expert systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.365

H-Index - 38

eISSN - 1468-0394

pISSN - 0266-4720

DOI - 10.1111/1468-0394.00253

Subject(s) - rough set , probabilistic logic , computer science , rule induction , decision rule , probabilistic relevance model , entropy (arrow of time) , dominance based rough set approach , algebraic number , probabilistic analysis of algorithms , mathematics , data mining , artificial intelligence , mathematical analysis , physics , quantum mechanics

Probabilistic approaches to rough sets in granulation, approximation and rule induction are reviewed. The Shannon entropy function is used to quantitatively characterize partitions of a universe. Both algebraic and probabilistic rough set approximations are studied. The probabilistic approximations are defined in a decision‐theoretic framework. The problem of rule induction, a major application of rough set theory, is studied in probabilistic and information‐theoretic terms. Two types of rules are analyzed: the local, low order rules, and the global, high order rules.

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