A Topsis‐Based Approach for Prioritized Aggregation in Multi‐Criteria Decision‐Making Problems
Bibliographic record
Abstract
Abstract Aggregation in multi‐criteria decision‐making environments is a process of combining the values of a set of attributes into one representative value for the entire set of attributes. Many aggregation methods—ranging from the simple averaging approach to more sophisticated methods, such as ordered weighted averaging—have been applied in previous research. One challenge in aggregation arises in special cases of prioritized aggregation, wherein the prioritized relationships between attributes must be considered during aggregation. This paper presents a new approach to aggregating attributes with prioritized relationships. First, an overview of past research is conducted to identify different aggregation methods, classes and properties. Next, the concept of prioritized aggregation is explained in detail. A prioritized aggregation method utilizing the technique of order preference by similarity to ideal solution is then presented. Subsequently, the presented prioritized aggregation method is applied on an actual case study. According to the results, the aggregation method presented in this paper is, through the application of technique of order preference by similarity to ideal solution, capable of quantifying and considering the prioritized relationship between a set of attributes undergoing aggregation. Finally, conclusions are stated, and a discussion describing future work pertinent to this paper is presented. Copyright © 2016 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".