A novel neural network-based fuzzy ranking for decision problems in sustainable energy
Bibliographic record
Abstract
The development of energy modeling often comes hand-in-hand with essential decision-making issues. In the field of decision-making, ranking alternatives that are often represented in the form of fuzzy sets, becomes a critical issue with far-reaching implications that has attracted significant attention. The essence of the problem is to evaluate and rank a collection of alternatives under a finite number of weighted criteria. The criteria and the assessment of alternatives are represented by fuzzy sets, which in the sequel give rise to fuzzy sets describing ranking results. Owing to the incorporation of the inherent fuzziness in the alternatives to be ranked, the process of sorting has become intricate. Given this, we transform the ranking problem to a scheme of supervised learning in which the data are composed of pairs (family of fuzzy sets to be ranked, sequence of ranking). A neural network is learnt by minimizing the loss function implemented as a Kendall-tau distance function. Considering the format of this loss function, the optimization of the network is carried out with the use of particle swarm optimization. The proposed method exhibits a notable degree of originality, as it offers a systematic technique to assign rankings to fuzzy sets irrespective of their different types of membership functions. Through sampling, fuzzy sets are innovatively represented by their characteristics across a uniformly distributed domain. Two experimental studies, including synthetic data ranking and a real-world case on plant cultivation in Lithuania, are conducted to demonstrate the effectiveness of the proposed approach
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".