Reduced Complexity Rule Induction
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Learning Be Knowledge Acquisition. Pages 678-683.
Abstract
We present an architecture for rule induction that emp~~izes COf!~pact, reduce~-complexity rules. A new heunsuc technique for findmg a covering rule set of sample data. is described. This technique refmes a set of producuon rules by iteratively replacing a component of a rule with its single best replacement. A meth<?<I for .rule in~uction has been developed that combmes th1s covenng and refinement scheme with other techniques known to help reduce the complexity of rule sets •. su~~ as weakest-link pruning, resampling, and the Judicious use of linear discriminants Published results on several real-world datasets arrevi~wed where d~ision trees have performed relauvely poorly. It 1S shown that far simpler decision rules can be found with predictive performance that exceeds .those previously reported for various learning models, mcluding neural nets and decision trees.
@inproceedings{ijcai1991p99,
title = {Reduced Complexity Rule Induction},
author = {Weiss, Sholom M. and Indurkhya, Nitin},
booktitle = {Proceedings of the Twelfth International Joint Conference on
Artificial Intelligence, {IJCAI-91}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John Mylopoulos and Ray Reiter},
pages = {678--683},
year = {1991},
month = {8},
note = {Learning Be Knowledge Acquisition},
url = {https://ijcai.org/proceedings/1991/99},
}
TY - CPAPER TI - Reduced Complexity Rule Induction AU - Weiss, Sholom M. AU - Indurkhya, Nitin T2 - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91 PB - International Joint Conferences on Artificial Intelligence PY - 1991 SP - 678 EP - 683 UR - https://ijcai.org/proceedings/1991/99 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
