Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Intelligent Data Analysis: An Introduction
Intelligent Data Analysis: An Introduction
Rough Set Flow Graphs and Max - * Fuzzy Relation Equations in State Prediction Problems
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
On representation and analysis of crisp and fuzzy information systems
Transactions on rough sets VI
Flow graphs and decision algorithms
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Extended random sets for knowledge discovery in information systems
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Novel matrix forms of rough set flow graphs with applications to data integration
Computers & Mathematics with Applications
Decision trees and flow graphs
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
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
Evolutionary computation and rough set-based hybrid approach to rule generation
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Transactions on Rough Sets III
An efficient algorithm for inference in rough set flow graphs
Transactions on Rough Sets V
Flow graphs and decision tables with fuzzy attributes
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Sufficiently Near Neighbourhoods of Points in Flow Graphs. A Near Set Approach
Fundamenta Informaticae - Cognitive Informatics and Computational Intelligence: Theory and Applications
Adaptive Method of Adjusting Flowgraph for Route Reconstruction in Video Surveillance Systems
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Some probabilistic properties of decision algorithms composed of "if ..., then ..." decision rules are considered. With every decision rule three probabilities are associated: the strength, the certainty and the coverage factors of the rule. It has been shown previously that the certainty and the coverage factors are linked by Bayes' theorem. Bayes' theorem has also been presented in a simple form employing the strength of decision rules. In this paper, we relax some conditions on the decision algorithm, in particular, a condition on mutual exclusion of decision rules, and show that the former properties still hold. We also show how the total probability theorem is related with modus ponens and modus tollens inference rules when decision rules are true in some degree of the certainty factor. Moreover, we show that under the relaxed condition, with every decision algorithm a flow graph can be associated, giving a useful interpretation of decision algorithms.