Rule-Based Regression
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 1072-1077.
Abstract
While decision trees have been used primarily for classification, they can also model regression or function approximation. Like classification trees, regression trees often yield excellent results with the advantages of strong explanatory capabilities and dymunic feature selection in high dimensions. In this paper, we contrast techniques for inducing classification and regression trees. We then present an altemative model of regression rules. A new technique for inducing decision rules for regression is described. This model of ordered disjunctive normal form rules, is potentially stronger and more compact than decision trees. Preliminary experimental results suggest that the new procedure is effective and can often exceed regression tree performance.
@inproceedings{ijcai1993p148,
title = {Rule-Based Regression},
author = {Weiss, Sholom M. and Indurkhya, Nitin},
booktitle = {Proceedings of the Thirteenth International Joint Conference on
Artificial Intelligence, {IJCAI-93}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ruzena Bajcsy},
pages = {1072--1077},
year = {1993},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1993/148},
}
TY - CPAPER TI - Rule-Based Regression AU - Weiss, Sholom M. AU - Indurkhya, Nitin T2 - Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93 PB - International Joint Conferences on Artificial Intelligence PY - 1993 SP - 1072 EP - 1077 UR - https://ijcai.org/proceedings/1993/148 ER -Copied
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