Simple data filtering in rough set systems

Autor(en): Duntsch, I
Gediga, G
Stichwörter: Computer Science; Computer Science, Artificial Intelligence; data filtering; rough sets; statistical validation
Erscheinungsdatum: 1998
Herausgeber: ELSEVIER SCIENCE INC
Journal: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volumen: 18
Ausgabe: 1-2
Startseite: 93
Seitenende: 106
Zusammenfassung: 
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. (C) 1998 Elsevier Science Inc.
ISSN: 0888613X
DOI: 10.1016/S0888-613X(97)10005-6

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