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IDDD: An inductive, domain dependent decision algorithm

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

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/29D53579-23C0-41DC-9F17-06164EF8A8CF
Έτος 1993
Τύπος Πλήρης Δημοσίευση σε Συνέδριο
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Βιβλιογραφική Αναφορά 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 https://doi.org/10.1007/3-540-56602-3_159
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Περίληψη

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.

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