Impute missing assessments by opinion clustering in multi-criteria group decision making problems

Ma, J; Zhang, G; Lu, J; Ruan, D

Impute missing assessments by opinion clustering in multi-criteria group decision making problems

Ma, J Zhang, G

Lu, J

Ruan, D

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Publication Type:: Conference Proceeding
Citation:: 2009 International Fuzzy Systems Association World Congress and 2009 European Society for Fuzzy Logic and Technology Conference, IFSA-EUSFLAT 2009 - Proceedings, 2009, pp. 555 - 560
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Ma, J	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Ruan, D	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	2009 International Fuzzy Systems Association World Congress and 2009 European Society for Fuzzy Logic and Technology Conference, IFSA-EUSFLAT 2009 - Proceedings, 2009, pp. 555 - 560	en_US
dc.identifier.isbn	9789899507968	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10760
dc.description.abstract	Multi-criteria group decision-making and evaluation (MCGDME) method typically aggregates information in evaluation tables. For various reasons, evaluation tables (decision matrix) often include missing data that highly affect correct decision-making and evaluation. Most existing imputation methods of missing data are based on statistical features which do not exist in an MCGDME setting. This paper proposes an imputation method of missing data (IMD) in evaluation tables. The IMD method measures the similarity betweent two evaluators' mental models. Evaluators are then classed into several groups based on their similarities by using fuzzy clustering methods. Finally, missing data are imputated under the assumption that the imputated value of missing data does not change the previous clustering results. The proposed IMD method is implemented and tested in two numerical experiments.	en_US
dc.relation.ispartof	2009 International Fuzzy Systems Association World Congress and 2009 European Society for Fuzzy Logic and Technology Conference, IFSA-EUSFLAT 2009 - Proceedings	en_US
dc.title	Impute missing assessments by opinion clustering in multi-criteria group decision making problems	en_US
dc.type	Conference Proceeding
utslib.for	0804 Data Format	en_US
dc.location.activity	Lisbon, Portugal	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Multi-criteria group decision-making and evaluation (MCGDME) method typically aggregates information in evaluation tables. For various reasons, evaluation tables (decision matrix) often include missing data that highly affect correct decision-making and evaluation. Most existing imputation methods of missing data are based on statistical features which do not exist in an MCGDME setting. This paper proposes an imputation method of missing data (IMD) in evaluation tables. The IMD method measures the similarity betweent two evaluators' mental models. Evaluators are then classed into several groups based on their similarities by using fuzzy clustering methods. Finally, missing data are imputated under the assumption that the imputated value of missing data does not change the previous clustering results. The proposed IMD method is implemented and tested in two numerical experiments.

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http://hdl.handle.net/10453/10760