Neurolinear: A system for extracting oblique decision rules from neural networks

Rudy Setiono, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

We present NeuroLinear, a system for extracting oblique decision rules from neural networks that have been trained for classification of patterns. Each condition of an oblique decision rule corresponds to a partition of the attribute space by a hyperplane that is not necessarily axis-parallel. Allowing a set of such hyperplanes to form the boundaries of the decision regions leads to a significant reduction in the number of rules generated while maintaining the accuracy rates of the networks. We describe the components of NeuroLinear in detail using a heart disease diagnosis problem. Our experimental results on real-world datasets show that the system is effective in extracting compact and comprehensible rules with high predictive accuracy from neural networks.

Original languageEnglish (US)
Title of host publicationMachine Learning
Subtitle of host publicationECML-97 - 9th European Conference on Machine Learning, Proceedings
EditorsMaarten van Someren, Gerhard Widmer, Gerhard Widmer
PublisherSpringer Verlag
Pages221-233
Number of pages13
ISBN (Print)3540628584, 9783540628583
StatePublished - Jan 1 1997
Externally publishedYes
Event9th European Conference on Machine Learning, ECML 1997 - Prague, Czech Republic
Duration: Apr 23 1997Apr 25 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1224
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th European Conference on Machine Learning, ECML 1997
CountryCzech Republic
CityPrague
Period4/23/974/25/97

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Setiono, R., & Liu, H. (1997). Neurolinear: A system for extracting oblique decision rules from neural networks. In M. van Someren, G. Widmer, & G. Widmer (Eds.), Machine Learning: ECML-97 - 9th European Conference on Machine Learning, Proceedings (pp. 221-233). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1224). Springer Verlag.