Journal
EXPERT SYSTEMS
Volume 20, Issue 5, Pages 305-310Publisher
WILEY
DOI: 10.1111/1468-0394.00255
Keywords
variable precision rough set model; VPRSM; probabilistic decision tables; data mining; machine learning
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The paper is concerned with the creation of predictive models from data within the framework of the variable precision rough set model. It is focused on two aspects of the model derivation: computation of uncertain, in general, rules from information contained in probabilistic decision tables and forming hierarchies of decision tables with the objective of reduction or elimination of decision boundaries in the resulting classifiers. A new technique of creation of a linearly structured hierarchy of decision tables is introduced and compared to tree-structured hierarchy. It is argued that the linearly structured hierarchy has significant advantages over tree-structured hierarchy.
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