Efficient Pruning Methods for Separate-and-Conquer Rule Learning Systems
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 988-993.
Abstract
Recent years have seen increased interest in systems that learn sets of rules. The goal of this paper is to study the degree to which "separate and conquer" rule learning induction methods scale up to large, real-world learning problems. In particular, we study the asymptotic complexity of rule induction on large training sets in the presence of noise. We present formal arguments and experimental data supporting the claim that existing methods do not scale up well on noisy data . We then present a solution in the form of new pruning techniques that dramatically improve the runtime of rule induction methods with no loss in accuracy: formal analysis shows an improvement in asymptotic time complexity, and experiments show an order-of-magnitude speedup on a set of benchmark problems while obtaining slightly more accurate hypotheses.
@inproceedings{ijcai1993p136,
title = {Efficient Pruning Methods for Separate-and-Conquer Rule Learning Systems},
author = {Cohen, William W.},
booktitle = {Proceedings of the Thirteenth International Joint Conference on
Artificial Intelligence, {IJCAI-93}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ruzena Bajcsy},
pages = {988--993},
year = {1993},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1993/136},
}
TY - CPAPER TI - Efficient Pruning Methods for Separate-and-Conquer Rule Learning Systems AU - Cohen, William W. T2 - Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93 PB - International Joint Conferences on Artificial Intelligence PY - 1993 SP - 988 EP - 993 UR - https://ijcai.org/proceedings/1993/136 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
