Genetic programming of fuzzy aggregation operations
Bibliographic record
Abstract
Aggregation operations play an important role in decision-making problems where a weighted combination of several criteria is used to select an alternative with the strongest support. In fuzzy set theory, aggregation operations are usually modeled as intersection, union, or as combination of both. The particular form and algebraic properties of these operations vary according to requirements for compensation among the criteria and other characteristics of the given decision-making situation. Traditionally, only algebraically well-behaved operations have been considered for this purpose. By relaxing some algebraic constraints, more realistic operations can be obtained that closely capture certain features of human decision-making, such as preferences and a limited level of detail. This paper proposes a method to generate fuzzy aggregation operations using genetic programming. It is shown that an evolutionary process, facilitated by genetic programming, has the capacity to generate new valid fuzzy aggregation operations and to reproduce existing ones. By varying process conditions, encoded in a fitness function, it is possible to obtain operations with different logical and algebraic properties. This approach, based solely on the axioms which define the desired class of operations, explores the space of possible functions and often leads to discovery of new operations. However, the proposed system can also be used to generate aggregation operations that fit a collected data set. This application is very important as it provides a powerful new tool for modeling and processing empirical data.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".