Premium
Classification and rule induction using rough set theory
Author(s) -
Bey Malcolm,
Curry Bruce,
Morgan Peter
Publication year - 2000
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/1468-0394.00136
Subject(s) - rough set , computer science , equivalence (formal languages) , rule induction , data mining , set (abstract data type) , metric (unit) , artificial intelligence , task (project management) , reduct , dominance based rough set approach , machine learning , mathematics , discrete mathematics , operations management , management , economics , programming language
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.