Abstract
The presentation is focused on the introduction and the investigation of probabilistic dependencies between attribute-defined partitions of a universe in hierarchies of probabilistic decision tables learned from data. The dependencies are expressed through two measures: the probabilistic generalization of the Pawlak’s measure of the dependency between attributes and the expected certainty gain measure. The expected certainty gain measure reflects the subtle grades of probabilistic dependence of events. The measures are reviewed and it is shown how they can be extended to dependencies existing in hierarchical structures of decision tables.
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Ziarko, W. (2007). Dependencies in Structures of Decision Tables. 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_13
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DOI: https://doi.org/10.1007/978-3-540-73451-2_13
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