A partial order for the M-of-N rule-extraction algorithm
- 1 November 1997
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 8 (6) , 1542-1544
- https://doi.org/10.1109/72.641475
Abstract
We present a method to unify the rules obtained by the M-of-N rule-extraction technique. The rules extracted from a perceptron by the M-of-N algorithm are in correspondence with sets of minimal Boolean vectors with respect to the classical partial order defined on vectors. Our method relies on a simple characterization of another partial order defined on Boolean vectors. We show that there exists also a correspondence between sets of minimal Boolean vectors with respect to this order and M-of-N rules equivalent to a perceptron. The gain is that fewer rules are generated with the second order. Independently, we prove that deciding whether a perceptron is symmetric with respect to two variables is NP-complete.Keywords
This publication has 5 references indexed in Scilit:
- Survey and critique of techniques for extracting rules from trained artificial neural networksPublished by Elsevier ,2000
- Extracting refined rules from knowledge-based neural networksMachine Learning, 1993
- Polynomial-time algorithms for regular set-covering and threshold synthesisDiscrete Applied Mathematics, 1985
- An Algorithm to Dualize a Regular Switching FunctionIEEE Transactions on Computers, 1979
- Coefficient reduction for inequalities in 0–1 variablesMathematical Programming, 1974