IDDD: An Inductive, Domain Dependent Decision Algorithm
Abstract
Decision tree induction, as supported by id 3, 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 id 3 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 id 3 itself.
Cite
Text
Gaga et al. "IDDD: An Inductive, Domain Dependent Decision Algorithm." European Conference on Machine Learning, 1993. doi:10.1007/3-540-56602-3_159Markdown
[Gaga et al. "IDDD: An Inductive, Domain Dependent Decision Algorithm." European Conference on Machine Learning, 1993.](https://mlanthology.org/ecmlpkdd/1993/gaga1993ecml-iddd/) doi:10.1007/3-540-56602-3_159BibTeX
@inproceedings{gaga1993ecml-iddd,
title = {{IDDD: An Inductive, Domain Dependent Decision Algorithm}},
author = {Gaga, Lena and Moustakis, Vassilis and Charissis, Giorgos and Orphanoudakis, Stelios C.},
booktitle = {European Conference on Machine Learning},
year = {1993},
pages = {408-413},
doi = {10.1007/3-540-56602-3_159},
url = {https://mlanthology.org/ecmlpkdd/1993/gaga1993ecml-iddd/}
}