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Abstract
This paper proposes a novel approach that adds the interpretability to Neural Knowledge DNA (NK-DNA) via generating a decision tree. The NK-DNA is a promising knowledge representation approach for acquiring, storing, sharing, and reusing knowledge among machines and computing systems. We introduce the decision tree-based generative method for knowledge extraction and representation to make the NK-DNA more explainable. We examine our approach through an initial case study. The experiment results show that the proposed method can transform the implicit knowledge stored in the NK-DNA into explicitly represented decision trees bringing fair interpretability to neural network-based intelligent systems.
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Authors (4)
-
Junjie Xiao
- Chengdu University, China
-
Tao Liu
- Chengdu University, China
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Haoxi Zhang
- Chengdu University, China
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- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2021.2018548
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
CYBERNETICS AND SYSTEMS
no. 53,
pages 500 - 509,
ISSN: 0196-9722 - Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Xiao J., Liu T., Zhang H., Szczerbicki E.: Adding Interpretability to Neural Knowledge DNA// CYBERNETICS AND SYSTEMS -Vol. 53,iss. 5 (2022), s.500-509
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2021.2018548
- Verified by:
- Gdańsk University of Technology
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