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Probabilistic approaches to rough sets

Yao, Y. Y.

Expert systems, 2003-11, Vol.20 (5), p.287-297 [Periódico revisado por pares]

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Título:
Probabilistic approaches to rough sets
Autor: Yao, Y. Y.
Assuntos: Artificial intelligence ; belief functions ; decision-theoretic rough set model ; Fuzzy set theory ; granular computing ; high order rules ; Knowledge acquisition ; rough set approximations ; Rough sets ; rule induction
É parte de: Expert systems, 2003-11, Vol.20 (5), p.287-297
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Descrição: : 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.
Editor: Oxford, UK and Boston, USA: Blackwell Publishing Ltd
Idioma: Inglês

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