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Comparative study of alternative types of knowledge reduction in inconsistent systems. (English) Zbl 0969.68146
Summary: Many types of attribute reduction and decision rules have been proposed in the area of rough sets. It is required to provide their consistent classification. The task is not easy because new proposals address different issues such as: noise in data, compact representation, prediction capability. Usually, when introducing a new knowledge reduction method the authors relate it only to one basic type of knowledge reduction. The main objective of the paper was to find and prove static relationships among classical types of knowledge reduction in inconsistent decision tables in order to provide an underlying classification of knowledge reduction types. Hence, if a newly devised reduction type is a specialization of some known type then all its properties inherited from generalized types will be also known.

MSC:
68T30 Knowledge representation
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