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Incomplete information tables and rough classification

dc.contributor.authorStefanowski, Jerzy
dc.contributor.authorTsoukiàs, Alexis
HAL ID: 740501
ORCID: 0000-0001-5772-3988
dc.date.accessioned2010-05-12T07:50:15Z
dc.date.available2010-05-12T07:50:15Z
dc.date.issued2002
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/4155
dc.language.isoenen
dc.subjectdecision rulesen
dc.subjectvalued tolerance relationen
dc.subjectsimilarity relationen
dc.subjectfuzzy setsen
dc.subjectrough setsen
dc.subjectincomplete informationen
dc.subject.ddc003en
dc.titleIncomplete information tables and rough classificationen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThe rough set theory, based on the original definition of the indiscernibility relation, is not useful for analysing incomplete information tables where some values of attributes are unknown. In this paper we distinguish two different semantics for incomplete information: the "missing value" semantics and the "absent value" semantics. The already known approaches, e.g. based on the tolerance relations, deal with the missing value case. We introduce two generalisations of the rough sets theory to handle these situations. The first generalisation introduces the use of a non symmetric similarity relation in order to formalise the idea of absent value semantics. The second proposal is based on the use of valued tolerance relations. A logical analysis and the computational experiments show that for the valued tolerance approach it is possible to obtain more informative approximations and decision rules than using the approach based on the simple tolerance relation.en
dc.relation.isversionofjnlnameComputational Intelligence
dc.relation.isversionofjnlvol17en
dc.relation.isversionofjnlissue3en
dc.relation.isversionofjnldate2002
dc.relation.isversionofjnlpages545-566en
dc.relation.isversionofdoihttp://dx.doi.org/10.1111/0824-7935.00162en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherWileyen
dc.subject.ddclabelRecherche opérationnelleen


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