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.
CITATION STYLE
Gaga, L., Moustakis, V., Charissis, G., & Orphanoudakis, S. (1993). IDDD: An inductive, domain dependent decision algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 667 LNAI, pp. 408–413). Springer Verlag. https://doi.org/10.1007/3-540-56602-3_159
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