Iterative rule induction methods

@article{Indurkhya1991IterativeRI,
  title={Iterative rule induction methods},
  author={Nitin Indurkhya and Sholom M. Weiss},
  journal={Applied Intelligence},
  year={1991},
  volume={1},
  pages={43-54}
}
We examine heuristic techniques for inducing production rules to cover artificially generated boolean expressions with irrelevant noise attributes. The results of different rule induction methods are compared, and it is shown that an iterative tree-based single-best-rule technique performs best on a set of widely-studied applications. We also introduce a new class of iterative Swap-1 rule induction techniques that also solve these problems. While the primary focus is on rule-based solutions… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 17 references

Classifieation and Regression Tress

L. Bre iman, J. F r i edman, R. Olshen, C. Stone
Wadswor th : Mon- terrey, • 1984
View 4 Excerpts
Highly Influenced

A n empir ical compar i son of pa t te rn recogni t ion, neural nets , and mach ine learn- ing c lass i f ica t ion m e t h o d s

S. Weiss
International Joint Con- ference on Artificial Intelligence, Detroit , • 1989
View 2 Excerpts

Filkin, "Efficient training of the back propagation network by solving a system of stiff ordinary differential equations,

A. Owens
in International Conference on Neural Networks, • 1989
View 1 Excerpt

Learning DNF by decision trees

IJCAI 1989 • 1989
View 3 Excerpts

Neural network learning time: effects of network and training set size

International 1989 Joint Conference on Neural Networks • 1989
View 1 Excerpt

The CN2 induction algorithm

Machine Learning • 1989
View 3 Excerpts