Decision Trees in Numerical Attribute Spaces
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 1016-1020.
Abstract
Research in Supervised Concept learning has not paid special attention to Numerical Attribute Spaces, although they present particular characteristics. One of them is that the combinatorial complexity of finding the correct disjunctions of the target concepts is much higher than with Nominal Attributes. In this paper, we argue that new kinds of heuristics using numerical similarities among training instances should be used. In the framework of TDIDT, we present a new approach that uses unsupervised heuristics issued from Cluster Analysis for selecting generalisation steps. Empirical tests show promising results, at least when the target concept is only partially represented in the training.
@inproceedings{ijcai1993p140,
title = {Decision Trees in Numerical Attribute Spaces},
author = {Van de Merckt, Thierry},
booktitle = {Proceedings of the Thirteenth International Joint Conference on
Artificial Intelligence, {IJCAI-93}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ruzena Bajcsy},
pages = {1016--1020},
year = {1993},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1993/140},
}
TY - CPAPER TI - Decision Trees in Numerical Attribute Spaces AU - Van de Merckt, Thierry T2 - Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93 PB - International Joint Conferences on Artificial Intelligence PY - 1993 SP - 1016 EP - 1020 UR - https://ijcai.org/proceedings/1993/140 ER -Copied
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