Encapsulated models for reasoning and decision support

dc.contributor.author	Čače, Ivana
dc.contributor.author	Meyer, John-Jules Ch.
dc.contributor.author	Pieterman, C.R.C.
dc.contributor.author	Valk, G.D.
dc.contributor.editor	Horbach, Matthias
dc.date.accessioned	2019-03-07T09:31:30Z
dc.date.available	2019-03-07T09:31:30Z
dc.date.issued	2013
dc.description.abstract	In this paper we show how knowledge from the medical domain can be incorporated in classification in a way that improves the transparency of classification (the 'why'), and makes the classification less dependent on both the particular data set used for training and on peculiarities of the classification algorithm. We compare a decision tree incorporating a domain model with a tree built directly from the same data.	en
dc.identifier.isbn	978-3-88579-614-5
dc.identifier.pissn	1617-5468
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/20614
dc.language.iso	en
dc.publisher	Gesellschaft für Informatik e.V.
dc.relation.ispartof	INFORMATIK 2013 – Informatik angepasst an Mensch, Organisation und Umwelt
dc.relation.ispartofseries	Lecture Notes in Informatics (LNI) - Proceedings, Volume P-220
dc.title	Encapsulated models for reasoning and decision support	en
dc.type	Text/Conference Paper
gi.citation.endPage	1833
gi.citation.publisherPlace	Bonn
gi.citation.startPage	1821
gi.conference.date	16.-20. September 2013
gi.conference.location	Koblenz
gi.conference.sessiontitle	Regular Research Papers

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