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dc.contributor.authorČače, Ivana
dc.contributor.authorMeyer, John-Jules Ch.
dc.contributor.authorPieterman, C.R.C.
dc.contributor.authorValk, G.D.
dc.contributor.editorHorbach, Matthias
dc.date.accessioned2019-03-07T09:31:30Z
dc.date.available2019-03-07T09:31:30Z
dc.date.issued2013
dc.identifier.isbn978-3-88579-614-5
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/20614
dc.description.abstractIn 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.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2013 – Informatik angepasst an Mensch, Organisation und Umwelt
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-220
dc.titleEncapsulated models for reasoning and decision supporten
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages1821-1833
mci.conference.sessiontitleRegular Research Papers
mci.conference.locationKoblenz
mci.conference.date16.-20. September 2013


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