G. Webb. Lecture Notes in Artificial Intelligence Vol. 406: Proceedings of the Second Australian Joint Conference on Artificial Intelligence (AI'88), page 225-239. Berlin, Springer-Verlag, (1988)
Abstract
This paper describes the LEI algorithm for empirical induction. The LEI algorithm provides efficient empirical induction for discrete attribute value data. It derives a classification procedure in the form of a set of predicate logic classification rules. This contrasts with the only other efficient approach to exhaustive empirical induction, the derivatives of the CLS algorithm, which present their classification procedures in the form of a decision tree. The LEI algorithm will always find the simplest non-disjunctive rule that correctly classifies all examples of a single class where such a rule exists.
%0 Conference Paper
%1 Webb88b
%A Webb, G. I.
%B Lecture Notes in Artificial Intelligence Vol. 406: Proceedings of the Second Australian Joint Conference on Artificial Intelligence (AI'88)
%C Berlin
%D 1988
%E Barter, C. J.
%E Brooks, M. J.
%I Springer-Verlag
%K Learning Rule
%P 225-239
%T Techniques for Efficient Empirical Induction
%X This paper describes the LEI algorithm for empirical induction. The LEI algorithm provides efficient empirical induction for discrete attribute value data. It derives a classification procedure in the form of a set of predicate logic classification rules. This contrasts with the only other efficient approach to exhaustive empirical induction, the derivatives of the CLS algorithm, which present their classification procedures in the form of a decision tree. The LEI algorithm will always find the simplest non-disjunctive rule that correctly classifies all examples of a single class where such a rule exists.
@inproceedings{Webb88b,
abstract = {This paper describes the LEI algorithm for empirical induction. The LEI algorithm provides efficient empirical induction for discrete attribute value data. It derives a classification procedure in the form of a set of predicate logic classification rules. This contrasts with the only other efficient approach to exhaustive empirical induction, the derivatives of the CLS algorithm, which present their classification procedures in the form of a decision tree. The LEI algorithm will always find the simplest non-disjunctive rule that correctly classifies all examples of a single class where such a rule exists.},
added-at = {2016-03-20T05:42:04.000+0100},
address = {Berlin},
audit-trail = {Reconstructed paper posted 6/6/05},
author = {Webb, G. I.},
biburl = {https://www.bibsonomy.org/bibtex/2474030599234df4d9de0bfe79e3df4b6/giwebb},
booktitle = {Lecture Notes in Artificial Intelligence Vol. 406: Proceedings of the Second Australian Joint Conference on Artificial Intelligence (AI'88)},
editor = {Barter, C. J. and Brooks, M. J.},
interhash = {5e647e5d57b77f18df3731f4dd983677},
intrahash = {474030599234df4d9de0bfe79e3df4b6},
keywords = {Learning Rule},
location = {Adelaide, S.A., Australia},
pages = {225-239},
publisher = {Springer-Verlag},
timestamp = {2016-03-20T05:42:04.000+0100},
title = {Techniques for Efficient Empirical Induction},
year = 1988
}