Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCC.2004.843201
DC FieldValue
dc.titleSeparating core and noncore knowledge: An application of neural network rule extraction to a cross-national study of brand image perception
dc.contributor.authorSetiono, R.
dc.contributor.authorPan, S.L.
dc.contributor.authorHsieh, M.-H.
dc.contributor.authorAzcarraga, A.P.
dc.date.accessioned2013-07-11T10:08:17Z
dc.date.available2013-07-11T10:08:17Z
dc.date.issued2005
dc.identifier.citationSetiono, R., Pan, S.L., Hsieh, M.-H., Azcarraga, A.P. (2005). Separating core and noncore knowledge: An application of neural network rule extraction to a cross-national study of brand image perception. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 35 (4) : 465-475. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCC.2004.843201
dc.identifier.issn10946977
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/42399
dc.description.abstractRecent 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TSMCC.2004.843201
dc.sourceScopus
dc.subjectBrand image perceptions
dc.subjectCore and noncore knowledge
dc.subjectM-of-N rules
dc.subjectNeural networks
dc.subjectRule extraction
dc.typeArticle
dc.contributor.departmentINFORMATION SYSTEMS
dc.description.doi10.1109/TSMCC.2004.843201
dc.description.sourcetitleIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
dc.description.volume35
dc.description.issue4
dc.description.page465-475
dc.description.codenITCRF
dc.identifier.isiut000232846700002
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