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IDDD: An inductive, domain dependent decision algorithm

Moustakis Vasilis, S. Orphanoudakis, G. Charissis, L. Gaga

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URIhttp://purl.tuc.gr/dl/dias/29D53579-23C0-41DC-9F17-06164EF8A8CF-
Identifierhttps://doi.org/10.1007/3-540-56602-3_159-
Languageen-
TitleIDDD: An inductive, domain dependent decision algorithmen
CreatorMoustakis Vasilisen
CreatorΜουστακης Βασιληςel
Creator S. Orphanoudakisen
CreatorG. Charissisen
CreatorL. Gagaen
PublisherSpringer Verlagen
Content SummaryDecision tree induction, as supported by id3, is a well known approach of heuristic classification. In this paper we introduce mother-child relationships to model dependencies between attributes which are used to represent, training examples. Such relationships are implemented via iddd which extends the original id3 algorithm. The application of iddd is demonstrated via a series of concept acquisition experiments using a ‘real-world’ medical domain. Results demonstrate that the application of iddd contributes to the acquisition of more domain relevant knowledge as compared to knowledge induced by id3 itself.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-04-
Date of Publication1993-
Bibliographic CitationL. Gaga, V. Moustakis, G. Charissis and S. Orphanoudakis, "IDDD: An Inductive Domain Dependent Algorithm," in European Conference on Machine Learning, 1993, pp. 408-413. doi: 10.1007/3-540-56602-3_159en

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