The Control of Membrane Thickness in PECVD Process Utilizing a Rule Extraction Technique of Neural Networks

2006 ◽  
pp. 1091-1098 ◽  
Author(s):  
Ming Chang ◽  
Jen-Cheng Chen ◽  
Jia-Sheng Heh
Keyword(s):  
Neural Networks ◽  
Rule Extraction ◽  
Extraction Technique ◽  
Membrane Thickness

scholarly journals A Rule Extraction Technique Applied to Ensembles of Neural Networks, Random Forests, and Gradient-Boosted Trees

Algorithms ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 339
Author(s):  
Guido Bologna
Keyword(s):  
Neural Networks ◽  
Decision Trees ◽  
Random Forests ◽  
Rule Extraction ◽  
Complex Problem ◽  
Extraction Technique ◽  
Classification Problems ◽  
Learning Ensembles ◽  
Fold Cross Validation ◽  
Natural Way

In machine learning, ensembles of models based on Multi-Layer Perceptrons (MLPs) or decision trees are considered successful models. However, explaining their responses is a complex problem that requires the creation of new methods of interpretation. A natural way to explain the classifications of the models is to transform them into propositional rules. In this work, we focus on random forests and gradient-boosted trees. Specifically, these models are converted into an ensemble of interpretable MLPs from which propositional rules are produced. The rule extraction method presented here allows one to precisely locate the discriminating hyperplanes that constitute the antecedents of the rules. In experiments based on eight classification problems, we compared our rule extraction technique to “Skope-Rules” and other state-of-the-art techniques. Experiments were performed with ten-fold cross-validation trials, with propositional rules that were also generated from ensembles of interpretable MLPs. By evaluating the characteristics of the extracted rules in terms of complexity, fidelity, and accuracy, the results obtained showed that our rule extraction technique is competitive. To the best of our knowledge, this is one of the few works showing a rule extraction technique that has been applied to both ensembles of decision trees and neural networks.

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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