Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the Desirability of Acyclic Database Schemes
Journal of the ACM (JACM)
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Probabilistic Expert Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Uncertain Information Processing in Expert Systems
Uncertain Information Processing in Expert Systems
In Pursuit of Patterns in Data Reasoning from Data The Rough Set Way
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Generalized Decision Algorithms, Rough Inference Rules, and Flow Graphs
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Flow graphs and decision algorithms
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
The computational complexity of inference using rough set flow graphs
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
On the implication problem for probabilistic conditional independency
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Inference and Reformation in Flow Graphs Using Granular Computing
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Extended Pawlak's Flow Graphs and Information Theory
Transactions on Computational Science V
Entropy Measures of Flow Graphs with Applications to Decision Trees
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Interpretation of extended Pawlak flow graphs using granular computing
Transactions on rough sets VIII
Novel matrix forms of rough set flow graphs with applications to data integration
Computers & Mathematics with Applications
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Pawlak recently introduced rough set flow graphs (RSFGs) as a graphical framework for reasoning from data. No study, however, has yet investigated the complexity of the accompanying inference algorithm, nor the complexity of inference in RSFGs. In this paper, we show that the traditional RSFG inference algorithm has exponential time complexity. We then propose a new RSFG inference algorithm that exploits the factorization in a RSFG. We prove its correctness and establish its polynomial time complexity. In addition, we show that our inference algorithm never does more work than the traditional algorithm. Our discussion also reveals that, unlike traditional rough set research, RSFGs make implicit independency assumptions regarding the problem domain.