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.
- Rule extraction
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence