NeuroLinear: A System for Extracting Oblique Decision Rules from Neural Networks
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.
Cite
Text
Setiono and Liu. "NeuroLinear: A System for Extracting Oblique Decision Rules from Neural Networks." European Conference on Machine Learning, 1997. doi:10.1007/3-540-62858-4_87Markdown
[Setiono and Liu. "NeuroLinear: A System for Extracting Oblique Decision Rules from Neural Networks." European Conference on Machine Learning, 1997.](https://mlanthology.org/ecmlpkdd/1997/setiono1997ecml-neurolinear/) doi:10.1007/3-540-62858-4_87BibTeX
@inproceedings{setiono1997ecml-neurolinear,
title = {{NeuroLinear: A System for Extracting Oblique Decision Rules from Neural Networks}},
author = {Setiono, Rudy and Liu, Huan},
booktitle = {European Conference on Machine Learning},
year = {1997},
pages = {221-233},
doi = {10.1007/3-540-62858-4_87},
url = {https://mlanthology.org/ecmlpkdd/1997/setiono1997ecml-neurolinear/}
}