Neurolinear: From neural networks to oblique decision rules

Rudy Setiono, Huan Liu

Research output: Contribution to journalArticle

75 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 by way of two examples using artificial datasets. 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)
Pages (from-to)1-24
Number of pages24
JournalNeurocomputing
Volume17
Issue number1
DOIs
StatePublished - Sep 30 1997
Externally publishedYes

Keywords

  • Discretization
  • Oblique-rule
  • Pruning
  • Rule extraction

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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