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Induction of decision rules in classification and discovery-oriented perspectives

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Date
2001
Dewey
Programmation, logiciels, organisation des données
Sujet
Système expert; Base connaissance; Classification
Journal issue
International Journal of Intelligent Systems
Volume
16
Number
1
Publication date
2001
Article pages
13-27
Publisher
Wiley
DOI
http://dx.doi.org/10.1002/1098-111X(200101)16:1<13::AID-INT3>3.0.CO;2-M
URI
https://basepub.dauphine.fr/handle/123456789/1626
Collections
  • LAMSADE : Publications
Metadata
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Author
Stefanowski, Jerzy
Vanderpooten, Daniel
Type
Article accepté pour publication ou publié
Abstract (EN)
This paper discusses induction of decision rules from data tables representing information about a set of objects described by a set of attributes. If the input data contains inconsistencies, rough sets theory can be used to handle them. The most popular perspectives of rule induction are classification and knowledge discovery. The evaluation of decision rules is quite different depending on the perspective. Criteria for evaluating the quality of a set of rules are presented and discussed. The degree of conflict and the possibility of achieving a satisfying compromise between criteria relevant to classification and criteria relevant to discovery are then analyzed. For this purpose, we performed an extensive experimental study on several well-known data sets where we compared two different approaches: (1) the popular rough set based rule induction algorithm LEM2 generating classification rules, (2) our own algorithm Explore - specific for discovery perspective.

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