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Classification and rule induction using rough set theory
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DOI：10.1111/1468-0394.00136.png)
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Abstract
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Rough set theory (RST) offers an interesting and novel approach both to the generation of rules for list 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.
Keywords:
rough sets
classification
decision tables
rule induction
set approximation
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