Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61883
Title: When Neural Networks Meet Decisional DNA: A Promising New Perspective for Knowledge Representation and Sharing
Contributor(s): Zhang, Haoxi (author); Sanin, Cesar  (author)orcid ; Szczerbicki, Edward (author)
Publication Date: 2016
Early Online Version: 2016-02-09
DOI: 10.1080/01969722.2016.1128776
Handle Link: https://hdl.handle.net/1959.11/61883
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 decision-making problems.

Publication Type: Journal Article
Source of Publication: Cybernetics and Systems, 47(1-2), p. 140-148
Publisher: Taylor & Francis Inc
Place of Publication: United States of America
ISSN: 1087-6553
0196-9722
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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