Comparative Analysis of Re-Identification Methods of Multi-Criteria Decision Analysis Models
One of the major hurdles in Multi-Criteria Decision Analysis (MCDA) is the re-identification of pre-existing decision models. Due to factors like limited access to domain experts, some models become impractical, leading to the need for methods that aid in their re-identification. Addressing the chal...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10819372/ |
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| Summary: | One of the major hurdles in Multi-Criteria Decision Analysis (MCDA) is the re-identification of pre-existing decision models. Due to factors like limited access to domain experts, some models become impractical, leading to the need for methods that aid in their re-identification. Addressing the challenge of re-identifying decision models brings up issues related to updating the last version of models to preserve their effectiveness, particularly in nonlinear decision scenarios. Common MCDA methods, which are based on linearity assumptions, encounter difficulties when dealing with nonlinearity, making it necessary to investigate practical methods for re-identifying nonlinear models. In this paper, we present innovative methods for re-identifying MCDA models utilizing optimization and machine learning techniques. Firstly, we introduce Support Vector Regression - Characteristic Objects Method (SVR-COMET), which combines the SVR and COMET methods. Secondly, we developed several extensions of the Stochastic Identification of Weights (SITW) algorithm. These methods were evaluated against a benchmark comprising four selected MCDA techniques. Comparisons were performed using Spearman’s weighted correlation coefficient (<inline-formula> <tex-math notation="LaTeX">$r_{w}$ </tex-math></inline-formula>). The findings from the study indicate that the proposed methods for re-identifying MCDA models are capable of mapping both linear and non-linear decision-making models. |
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| ISSN: | 2169-3536 |