Fuzzy Grey Choquet Integral for Evaluation of Multicriteria Decision Making Problems With Interactive and Qualitative Indices
Why this work is in the frame
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Bibliographic record
Abstract
Multicriteria decision making (MCDM) problems are often encountered in complex system design. Most of them need to be evaluated with a large number of interactive and qualitative indices, which are difficult to be addressed effectively through the existing methods. In this paper, a novel fuzzy Choquet integral-based grey comprehensive evaluation (GCE) method, called fuzzy grey Choquet integral (FGCI), is proposed to evaluate MCDM problems with many interactive and qualitative indices. In this method, expert evaluation of qualitative indices is represented through fuzzy linguistic values. Fuzzy values are defuzzified and standardized to obtain the original evaluation matrix. The original values are replaced by the correlation coefficients, which, to a certain extent, eliminate the influence of experts' subjective preference. An improved teaching-learning-based optimization algorithm is employed to identify λ-fuzzy-measures following the weights given by experts in order to enhance the consistency of weights. Then the correlation coefficients are aggregated through Choquet integral among λ-fuzzy-measures, which can reflect interactions among indices. In addition, according to the characteristics of λ-fuzzy-measures, the construction guidelines for a corresponding index system are given to overcome the limitations of FGCI. Finally, the performance of the proposed method is demonstrated via a practical example of green design evaluation and compared with the GCE method. The results validate its feasibility and effectiveness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it