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

Publisher:
IFSA/EUSFLAT
Publication Type:
Conference Proceeding
Citation:
The 13th IFSA World Congress and the 6th Conference of EUSFLAT, 2009, pp. 555 - 560
Issue Date:
2009-01
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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.
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