Abstract
The paper describes knowledge acquisition under uncertainty using rough set theory, a concept introduced by Z. Pawlak in 1981. A collection of rules is acquired, on the basis of information stored in a data base-like system, called an information system. Uncertainty implies inconsistencies, which are taken into account, so that the produced rules are categorized into certain and possible with the help of rough set theory. The approach presented belongs to the class of methods of learning from examples. The taxonomy of all possible expert classifications, based on rough set theory, is also established. It is shown that some classifications are theoretically (and, therefore, in practice) forbidden.
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Grzymala-Busse, J.W. Knowledge acquisition under uncertainty — a rough set approach. J Intell Robot Syst 1, 3–16 (1988). https://doi.org/10.1007/BF00437317
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DOI: https://doi.org/10.1007/BF00437317