Title: Simple data filtering in rough set systems
Authors: Ivo Düntsch , Dept of Computer Science , Brock University , St Catherines, Ontario, L2S 3A1, Canada
Günther Gediga , Institut für Evaluation und Marktanalysen; Brinkstr. 19; D-49143 Jeggen; Germany
(Equal authorship implied)
Status: International Journal of Approximate Reasoning 18 (1998), 93-106
Abstract: In symbolic data analysis, high granularity of information may lead to rules based on a few cases only for which there is no evidence that they are not due to random choice, and thus have a doubtful validity.

We suggest a simple way to improve the statistical strength of rules obtained by rough set data analysis by identifying attribute values and investigating the resulting information system. This enables the researcher to reduce the granularity within attributes without assuming external structural information such as probability distributions or fuzzy membership functions.

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