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Classification and rule induction using rough set theory

Beynon, Malcolm James ORCID:, Curry, Bruce and Morgan, Peter Huw ORCID: 2000. Classification and rule induction using rough set theory. Expert Systems 17 (3) , pp. 136-148. 10.1111/1468-0394.00136

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Rough set theory (RST) offers an interesting and novel approach both to the generation of rules for use in expert systems and to the traditional statistical task of classification. The method is based on a novel classification metric, implemented as upper and lower approximations of a set and more generally in terms of positive, negative and boundary regions. Classification accuracy, which may be set by the decision maker, is measured in terms of conditional probabilities for equivalence classes, and the method involves a search for subsets of attributes (called ’reducts’) which do not require a loss of classification quality. To illustrate the technique, RST is employed within a state level comparison of education expenditure in the USA.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords: rough sets; classification; decision tables; rule induction; set approximation
Publisher: Wiley-Blackwell
ISSN: 0266-4720
Last Modified: 21 Oct 2022 09:48

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