Abstract
Expert systems are problem solvers for specialized domains of competence in which effective problem solving normally requires human expertise. Rough set theory is intelligent technique used in the discovery of data dependencies; it evaluates the importance of attributes, reduces all redundant objects and attributes. Moreover, it is being used for the extraction of rules from databases. Expert systems are often implemented using knowledge discovered in data bases, for example, using rough set based rule generation method. When we try to analyze large, possibly hierarchical rule sets, we often seek useful graphical representation.
A proposition of such graphical representation decision networks we can find in [4]. The main aim of this work is to present own graph-based rule base representation method and its utilization for verification task. The paper firstly presents the decision units conception needed to establish our verification approach. Next we present the usage of decision units net in global verification and modeling issues, and some remarks on decision units and data mining. The last chapter draws the main conclusions.
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Simiński, R. (2007). Graph-Based Knowledge Representations for Decision Support Systems. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_46
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DOI: https://doi.org/10.1007/978-3-540-73451-2_46
Publisher Name: Springer, Berlin, Heidelberg
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