Rule-based systems and pattern recognition
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Machine Learning
Comparative Performance of Rule Quality Measures in an InductionSystem
Applied Intelligence
Machine Learning
Induction of Rules Subject to a Quality Constraint: Probabilistic Inductive Learning
IEEE Transactions on Knowledge and Data Engineering
An Empirical Study on Rule Quality Measures
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
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A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
Case- and rule-based algorithms for the contextual pattern recognition problem
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartI
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Paper deals with the knowledge acquisition process for the design of the decision support system. Usually in this case the knowledge is given in the form of rules which are formulated by human experts or/and generated on the basis of datasets. Each of experts has different knowledge about the problem under consideration and rules formulated by them have different qualities. The qualities of data stored in the databases are different as well. It might cause differences in quality of generated rules. In the paper we formulate the proposition of a knowledge source confidence measure and we show some of its applications to the decision process e.g., we show how to use it for contradiction elimination in the set of rule. Additionally, we propose how it could be used during decision making.