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Summary: PERGAMON Computers and Mathematics Applications 45 (2003)
An lntemationalJournal
computers&
mathematics
withqlpliccltlw
737-748
www.elsevier.com/locate/camwa
Learning Classification Rules
from Data
A. AN
Department of Computer Science
York University
Toronto, Ontario M3J lP3 Canada
aanBcs.yorku.ca
(Received October 2001; accepted January 2002)
Abstract-we present ELEMZ, a machine learning system that induces classification rules from
a set of data based on a heuristic search over a hypothesis space. ELEM2 is distinguished from
other rule induction systems in three aspects. First, it uses a new heuristic function to guide the
heuristic search. The function reflects the degree of relevance of an attribute-value pair to a target
concept and leads to selection of the most relevant pairs for formulating rules. Second, ELEM2
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