IDDD: An inductive, domain dependent decision algorithm

URI	http://purl.tuc.gr/dl/dias/29D53579-23C0-41DC-9F17-06164EF8A8CF	-
Identifier	https://doi.org/10.1007/3-540-56602-3_159	-
Language	en	-
Title	IDDD: An inductive, domain dependent decision algorithm	en
Creator	Moustakis Vasilis	en
Creator	Μουστακης Βασιλης	el
Creator	S. Orphanoudakis	en
Creator	G. Charissis	en
Creator	L. Gaga	en
Publisher	Springer Verlag	en
Content Summary	Decision 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 Item	Πλήρης Δημοσίευση σε Συνέδριο	el
Type of Item	Conference Full Paper	en
License	http://creativecommons.org/licenses/by/4.0/	en
Date of Item	2015-11-04	-
Date of Publication	1993	-
Bibliographic Citation	L. 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_159	en

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