Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0925-2312(97)00038-6
Title: Neurolinear: From neural networks to oblique decision rules
Authors: Setiono, R. 
Liu, H. 
Keywords: Discretization
Oblique-rule
Pruning
Rule extraction
Issue Date: 30-Sep-1997
Citation: Setiono, R., Liu, H. (1997-09-30). Neurolinear: From neural networks to oblique decision rules. Neurocomputing 17 (1) : 1-24. ScholarBank@NUS Repository. https://doi.org/10.1016/S0925-2312(97)00038-6
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.
Source Title: Neurocomputing
URI: http://scholarbank.nus.edu.sg/handle/10635/99345
ISSN: 09252312
DOI: 10.1016/S0925-2312(97)00038-6
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