@inproceedings{b0edde345cfb48fab01ff20aca0481b1,
title = "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.",
author = "Rudy Setiono and Huan Liu",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1997.; 9th European Conference on Machine Learning, ECML 1997 ; Conference date: 23-04-1997 Through 25-04-1997",
year = "1997",
doi = "10.1007/3-540-62858-4_87",
language = "English (US)",
isbn = "3540628584",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "221--233",
editor = "{van Someren}, Maarten and Gerhard Widmer",
booktitle = "Machine Learning",
}