On learning and evaluation of decision rules in the context of rough sets
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This paper constitutes an account of the authors' experiences and a presentation of results obtained in a real-life application of rough set theory's methods and techniques in the identification and analysis of data dependencies and relationships on an empirical data. The data was collected in the course of an experiment on the biological control of the Leafy Spurge [13] weed in the prairies of Western Canada using an agent beetle known as Aphthona nigriscutis (A. n.). The rough set theory was applied to the data in order to identify and analyze the different factors affecting the success of the biological control of the host weed. This led to the discovery and confirmation of meaningful patterns and the computation of a set of rules for the critical application of agent beetle A. n in the control of Leafy Spurge weed.