JACIII Vol.3 No.5 pp. 348-356
doi: 10.20965/jaciii.1999.p0348


Hybrid Neural-global Minimization Method of Logical Rule Extraction

Wlodzislaw Duch, Rafal Adamczak, KrzysAof Grabczewski and Grzegorz Zal

Department of Computer Methods, Nicolas Copernicus University, Grudziadzka 5, 87-100 Torun, Poland

May 7,1999
September 20, 1999
October 20, 1999
Computational intelligence, Neural networks, Extraction of logical rules, Data mining

Methodology of extraction of optimal sets of logical rules using neural networks and global minimization procedures has been developed. Initial rules are extracted using density estimation neural networks with rectangular functions or multilayered perceptron (MLP) networks trained with constrained backpropagation algorithm, transforming MLPs into simpler networks performing logical functions. A constructive algorithm called CMLP2LN is proposed, in which rules of increasing specificity are generated consecutively by adding more nodes to the network. Neural rule extraction is followed by optimization of rules using global minimization techniques. Estimation of confidence of various sets of rules is discussed. The hybrid approach to rule extraction has been applied to a number of benchmark and real life problems with very good results.

Cite this article as:
Wlodzislaw Duch, Rafal Adamczak, KrzysAof Grabczewski, and Grzegorz Zal, “Hybrid Neural-global Minimization Method of Logical Rule Extraction,” J. Adv. Comput. Intell. Intell. Inform., Vol.3, No.5, pp. 348-356, 1999.
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