The Computational Complexity of Inference Using Rough Set Flow Graphs | Zendy

Cory J. Butz | Zendy; Wen Yan | Zendy; Boting Yang | Zendy

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The Computational Complexity of Inference Using Rough Set Flow Graphs

Author(s) -

Cory J. Butz,

Wen Yan,

Boting Yang

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-28653-5

DOI - 10.1007/11548669_35

Subject(s) - rough set , inference , bayesian network , computer science , graphical model , bayes' theorem , domain (mathematical analysis) , influence diagram , set (abstract data type) , bayesian inference , theoretical computer science , mathematics , rule of inference , algorithm , data mining , bayesian probability , machine learning , artificial intelligence , decision tree , mathematical analysis , programming language

Pawlak recently introduced rough set flow graphs (RSFGs) as a graphical framework for reasoning from data. Each rule is associated with three coefficients, which have been shown to satisfy Bayes' theorem. Thereby, RSFGs provide a new perspective on Bayesian inference methodology. In this paper, we show that inference in RSFGs takes polynomial time with respect to the largest domain of the variables in the decision tables. Thereby, RSFGs provide an efficient tool for uncertainty management. On the other hand, our analysis also indicates that a RSFG is a special case of conventional Bayesian network and that RSFGs make implicit assumptions regarding the problem domain.

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