Workflow for Knowledge Extraction from Neural Network Classifiers
2018 59th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2018)
2018
Andrejs Bondarenko,
Ludmila Aleksejeva
Artificial neural network classifiers are
widespread models used by many machine learning engineers.
Although due to fact they are black box models, in mission
critical areas (like healthcare, finance, atomic power) when
explainability is required they cannot be used even when they
show higher classification performance in comparison to
explainable models like decision trees. To mitigate this problem
knowledge extraction algorithms have been proposed allowing
to extract knowledge in different forms. Current paper gives a
review of three knowledge extraction algorithms, presents their
strengths and weaknesses. Finally knowledge extraction
workflow utilizing abovementioned algorithms is described.
Keywords
Artificial neural network, feedforward neural networks, knowledge acquisition, radial basis function neural networks
DOI
10.1109/ITMS.2018.8552964
Hyperlink
https://ieeexplore-ieee-org.resursi.rtu.lv/document/8552964
Bondarenko, A., Aleksejeva, L. Workflow for Knowledge Extraction from Neural Network Classifiers. In: 2018 59th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2018), Latvia, Rīga, 10-12 October, 2018. Piscataway: IEEE, 2018, pp.57-62. ISBN 978-1-7281-0099-9. e-ISBN 978-1-7281-0098-2. Available from: doi:10.1109/ITMS.2018.8552964
Publication language
English (en)