Methodology for Knowledge Extraction from Trained Artificial Neural Networks

Andrey Bondarenko, Ludmila Aleksejeva


Artificial neural networks are widely spread models that outperform more basic, but explainable machine learning models like classification decision tree. Although their lack of explainability severely limits their area of application. All mission critical areas or law regulated areas (like European GDPR) require model to be explained. Explainability allows model validation for correctness and lack of bias. Thus methods for knowledge extraction from artificial neural networks have gained attention and development efforts. Current paper addresses this problem and describes knowledge extraction methodology which can be applied to classification problems. It is based on previous research and allows knowledge to be extracted from trained fully connected feed-forward artificial neural network, from radial basis function neural network and from hyper-polytope based classifier in the form of binary classification decision tree, elliptical rules and If-Then rules.


Artificial neural network; feed-forward neural networks; knowledge acquisition; knowledge extraction; radial basis function neural networks

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DOI: 10.7250/itms-2018-0001


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