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

Research output: Contribution to report/book/conference proceedingsIn-proceedings paper

Standard

Impute missing assessments by opinion clustering in multi-criteria group decision making problems. / Ma, Jun; Zhang, Guangquan; Lu, Jie; Ruan, Da; Kabak, Özgür (Peer reviewer).

IFSA/EUSFLAT 2009. Vol. 1 Lisbon, Portugal, 2009. p. 555-560.

Research output: Contribution to report/book/conference proceedingsIn-proceedings paper

Harvard

Ma, J, Zhang, G, Lu, J, Ruan, D & Kabak, Ö 2009, Impute missing assessments by opinion clustering in multi-criteria group decision making problems. in IFSA/EUSFLAT 2009. vol. 1, Lisbon, Portugal, pp. 555-560, 2009 World Congress International Fuzzy Systems Association, Lisbon, Portugal, 2009-07-20.

APA

Ma, J., Zhang, G., Lu, J., Ruan, D., & Kabak, Ö. (2009). Impute missing assessments by opinion clustering in multi-criteria group decision making problems. In IFSA/EUSFLAT 2009 (Vol. 1, pp. 555-560). Lisbon, Portugal.

Vancouver

Ma J, Zhang G, Lu J, Ruan D, Kabak Ö. Impute missing assessments by opinion clustering in multi-criteria group decision making problems. In IFSA/EUSFLAT 2009. Vol. 1. Lisbon, Portugal. 2009. p. 555-560

Author

Ma, Jun ; Zhang, Guangquan ; Lu, Jie ; Ruan, Da ; Kabak, Özgür. / Impute missing assessments by opinion clustering in multi-criteria group decision making problems. IFSA/EUSFLAT 2009. Vol. 1 Lisbon, Portugal, 2009. pp. 555-560

Bibtex - Download

@inproceedings{02b301a6c0d940df87c915b3fd232014,
title = "Impute missing assessments by opinion clustering in multi-criteria group decision making problems",
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.",
keywords = "decision making, missing data, multi-criteria evaluation, opinion clustering, aggregation",
author = "Jun Ma and Guangquan Zhang and Jie Lu and Da Ruan and {\"O}zg{\"u}r Kabak",
note = "Score = 3",
year = "2009",
month = "7",
language = "English",
isbn = "978-989-95079-6-8",
volume = "1",
pages = "555--560",
booktitle = "IFSA/EUSFLAT 2009",

}

RIS - Download

TY - GEN

T1 - Impute missing assessments by opinion clustering in multi-criteria group decision making problems

AU - Ma, Jun

AU - Zhang, Guangquan

AU - Lu, Jie

AU - Ruan, Da

A2 - Kabak, Özgür

N1 - Score = 3

PY - 2009/7

Y1 - 2009/7

N2 - 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.

AB - 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.

KW - decision making

KW - missing data

KW - multi-criteria evaluation

KW - opinion clustering

KW - aggregation

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_99956

UR - http://knowledgecentre.sckcen.be/so2/bibref/6038

M3 - In-proceedings paper

SN - 978-989-95079-6-8

VL - 1

SP - 555

EP - 560

BT - IFSA/EUSFLAT 2009

CY - Lisbon, Portugal

ER -

ID: 62957