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When Neural Networks Meet Decisional DNA: A Promising New Perspective for Knowledge Representation and Sharing
Abstract
ABSTRACT In this article, we introduce a novel concept combining neural network technology and Decisional DNA for knowledge representation and sharing. Instead of using traditional machine learning and knowledge discovery methods, this approach explores the way of knowledge extraction through deep learning processes based on a domain’s past decisional events captured by Decisional DNA. We compare our approach with kNN (k-nearest neighbors), logistic regression, and AdaBoost in classification tasks, and the results show that our approach is very promising with regard to the enhancement of the accuracy of knowledge-based predictions required in complex decisionmaking problems.
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Authors (3)
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Haoxi Zhang
- Chengdu University Computer Science
-
Cesar Sanin
- University of Newcastle School of Engineering
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- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2016.1128776
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Details
- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
CYBERNETICS AND SYSTEMS
no. 47,
edition 1-2,
pages 140 - 148,
ISSN: 0196-9722 - Language:
- English
- Publication year:
- 2016
- Bibliographic description:
- Zhang H., Sanin C., Szczerbicki E.: When Neural Networks Meet Decisional DNA: A Promising New Perspective for Knowledge Representation and Sharing// CYBERNETICS AND SYSTEMS. -Vol. 47, iss. 1-2 (2016), s.140-148
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2016.1128776
- Verified by:
- Gdańsk University of Technology
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