Induction of Oblique Decision Trees
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 1002-1006.
Abstract
This paper introduces a randomized technique for partitioning examples using oblique hyperplanes. Standard decision tree techniques, such as ID3 and its descendants, partition a set of points with axis-parallel hyperplanes. Our method, by contrast, attempts to find hyperplanes at any orientation. The purpose of this more general technique is to find smaller but equally accurate decision trees than those created by other methods. We have tested our algorithm on both real and simulated data, and found that in some cases it produces surprisingly small trees without losing predictive accuracy. Small trees allow us, in turn, to obtain simple qualitative descriptions of each problem domain.
@inproceedings{ijcai1993p138,
title = {Induction of Oblique Decision Trees},
author = {Heath, David G. and Kasif, Simon and Salzberg, Steven},
booktitle = {Proceedings of the Thirteenth International Joint Conference on
Artificial Intelligence, {IJCAI-93}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ruzena Bajcsy},
pages = {1002--1006},
year = {1993},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1993/138},
}
TY - CPAPER TI - Induction of Oblique Decision Trees AU - Heath, David G. AU - Kasif, Simon AU - Salzberg, Steven T2 - Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93 PB - International Joint Conferences on Artificial Intelligence PY - 1993 SP - 1002 EP - 1006 UR - https://ijcai.org/proceedings/1993/138 ER -Copied
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