scholarly journals Exact and Approximate Rule Extraction from Neural Networks with Boolean Features

2019 ◽  
Author(s):  
Fawaz Mereani ◽  
Jacob Howe
Keyword(s):  
Neural Networks ◽  
Rule Extraction

A search technique for rule extraction from trained neural networks

1999 ◽  
Vol 20 (3) ◽  
pp. 273-280 ◽  
Author(s):  
R. Krishnan ◽  
G. Sivakumar ◽  
P. Bhattacharya
Keyword(s):  
Neural Networks ◽  
Rule Extraction ◽  
Search Technique

scholarly journals Hybrid Neural-global Minimization Method of Logical Rule Extraction

1999 ◽  
Vol 3 (5) ◽  
pp. 348-356 ◽  
Author(s):  
Wlodzislaw Duch ◽  
◽  
Rafal Adamczak ◽  
KrzysAof Grabczewski ◽  
Grzegorz Zal
Keyword(s):  
Neural Networks ◽  
Hybrid Approach ◽  
Real Life ◽  
Rule Extraction ◽  
Global Minimization ◽  
Minimization Method ◽  
Life Problems ◽  
Multilayered Perceptron ◽  
Logical Functions ◽  
Logical Rules

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.

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