Separating core and noncore knowledge: An application of neural network rule extraction to a cross-national study of brand image perception

Recent advances in algorithms that extract rules from artificial neural networks make it feasible to use neural networks as a tool for acquiring knowledge hidden in the data. Findings are reported from the use of such algorithms to separate core and noncore knowledge in a cross-national study of aut...

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Main Authors: Setiono, Rudy, Pan, Shan L., Hsieh, Ming Huei, Azcarraga, Arnulfo P.
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Published: Animo Repository 2005
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/2374
https://animorepository.dlsu.edu.ph/context/faculty_research/article/3373/type/native/viewcontent
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-33732021-08-25T06:55:52Z Separating core and noncore knowledge: An application of neural network rule extraction to a cross-national study of brand image perception Setiono, Rudy Pan, Shan L. Hsieh, Ming Huei Azcarraga, Arnulfo P. Recent advances in algorithms that extract rules from artificial neural networks make it feasible to use neural networks as a tool for acquiring knowledge hidden in the data. Findings are reported from the use of such algorithms to separate core and noncore knowledge in a cross-national study of automobile brand image perception. Respondents from five Western European countries have been asked to associate individual and corporate brand associations for a number of well-known automobile brands. Knowledge, expressed as concise and accurate rules that distinguish between the respondents' perceptions of German and Japanese brands, is extracted from trained neural networks. This paper explains how both core knowledge, which captures the perceptions shared by the respondents in all countries, and country-specific noncore knowledge can be acquired and differentiated by a proposed two-step approach to train and extract rules from a multi-neural network system. The experimental results show that, in addition to providing a better understanding of the differences and similarities in the brand image perceptions of consumers in various countries, the proposed approach also yields better predictive accuracy than a decision tree method. © 2005 IEEE. 2005-11-01T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/2374 https://animorepository.dlsu.edu.ph/context/faculty_research/article/3373/type/native/viewcontent Faculty Research Work Animo Repository Brand choice Consumers' preferences Neural networks (Computer science) Sentiment analysis Computer Sciences
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Brand choice
Consumers' preferences
Neural networks (Computer science)
Sentiment analysis
Computer Sciences
spellingShingle Brand choice
Consumers' preferences
Neural networks (Computer science)
Sentiment analysis
Computer Sciences
Setiono, Rudy
Pan, Shan L.
Hsieh, Ming Huei
Azcarraga, Arnulfo P.
Separating core and noncore knowledge: An application of neural network rule extraction to a cross-national study of brand image perception
description Recent advances in algorithms that extract rules from artificial neural networks make it feasible to use neural networks as a tool for acquiring knowledge hidden in the data. Findings are reported from the use of such algorithms to separate core and noncore knowledge in a cross-national study of automobile brand image perception. Respondents from five Western European countries have been asked to associate individual and corporate brand associations for a number of well-known automobile brands. Knowledge, expressed as concise and accurate rules that distinguish between the respondents' perceptions of German and Japanese brands, is extracted from trained neural networks. This paper explains how both core knowledge, which captures the perceptions shared by the respondents in all countries, and country-specific noncore knowledge can be acquired and differentiated by a proposed two-step approach to train and extract rules from a multi-neural network system. The experimental results show that, in addition to providing a better understanding of the differences and similarities in the brand image perceptions of consumers in various countries, the proposed approach also yields better predictive accuracy than a decision tree method. © 2005 IEEE.
format text
author Setiono, Rudy
Pan, Shan L.
Hsieh, Ming Huei
Azcarraga, Arnulfo P.
author_facet Setiono, Rudy
Pan, Shan L.
Hsieh, Ming Huei
Azcarraga, Arnulfo P.
author_sort Setiono, Rudy
title Separating core and noncore knowledge: An application of neural network rule extraction to a cross-national study of brand image perception
title_short Separating core and noncore knowledge: An application of neural network rule extraction to a cross-national study of brand image perception
title_full Separating core and noncore knowledge: An application of neural network rule extraction to a cross-national study of brand image perception
title_fullStr Separating core and noncore knowledge: An application of neural network rule extraction to a cross-national study of brand image perception
title_full_unstemmed Separating core and noncore knowledge: An application of neural network rule extraction to a cross-national study of brand image perception
title_sort separating core and noncore knowledge: an application of neural network rule extraction to a cross-national study of brand image perception
publisher Animo Repository
publishDate 2005
url https://animorepository.dlsu.edu.ph/faculty_research/2374
https://animorepository.dlsu.edu.ph/context/faculty_research/article/3373/type/native/viewcontent
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