Rough sets, decision algorithms and Bayes' theorem

DOI10.1016/S0377-2217(01)00029-7zbMath1089.68127OpenAlexW2040745298MaRDI QIDQ5955099

Zdzisław Pawlak

Publication date: 2002

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0377-2217(01)00029-7

zbMATH Keywords

Decision support systemsDecision analysisBayes' theoremRough sets

Mathematics Subject Classification ID

Decision theory (91B06) Probability and inductive logic (03B48) Reasoning under uncertainty in the context of artificial intelligence (68T37) Computing methodologies for information systems (hypertext navigation, interfaces, decision support, etc.) (68U35)

Related Items (23)

Integration and application of rough sets and data envelopment analysis for assessments of the investment trusts industry ⋮ Double Successive Rough Set Approximations ⋮ Two Bayesian approaches to rough sets ⋮ Multiset concepts in two-universe approximation spaces ⋮ Subjective Weights Based Meta-Learning in Multi-criteria Decision Making ⋮ Rough set models ofPhysarummachines ⋮ Novel matrix forms of rough set flow graphs with applications to data integration ⋮ Consistency of probability decision rules and its inference in probability decision table ⋮ Decisions rules and flow networks. ⋮ Identification of structures and causation in flow graphs ⋮ A rough set-based incremental approach for learning knowledge in dynamic incomplete information systems ⋮ Two new types of multiple granulation rough set ⋮ Stochastic dominance-based rough set model for ordinal classification ⋮ Evaluation functions and decision conditions of three-way decisions with game-theoretic rough sets ⋮ Parameterized rough set model using rough membership and Bayesian confirmation measures ⋮ Knowledge structure, knowledge granulation and knowledge distance in a knowledge base ⋮ A probability maximization model based on rough approximation and its application to the inventory problem ⋮ Interpretation of Extended Pawlak Flow Graphs Using Granular Computing ⋮ Research on Rough Set Theory and Applications in China ⋮ Attribute reduction and optimal decision rules acquisition for continuous valued information systems ⋮ Can interestingness measures be usefully visualized? ⋮ The comparative study of covering rough sets and multi-granulation rough sets ⋮ On relationship between probabilistic rough set and Bayesian risk decision over two universes

Cites Work

• The logic of conditionals. An application of probability to deductive logic
• Rough sets in knowledge discovery 1. Methodology and applications
• Normative selection of Bayesian networks
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Rough sets, decision algorithms and Bayes' theorem