Bibliographic record
Abstract
Uncertainty and imprecision are intrinsic features of available data about the real world. An object is uncertain if we cannot make sure decisions about if the object belongs to a target concept. Three-way decisions provide three options, i.e., accept, reject, and non-commitment, in the face of uncertainty. All objects are divided into three regions according to which option is selected for each object. The determination of three-way decisions is a key issue of analyzing uncertain data. When multiple evaluation measures are involved, determining three-way decisions from a tradeoff perspective is a challenging process. The thesis focuses on determining three-way decisions from a tradeoff perspective by combining game theory with uncertain data analysis approaches, i.e., rough sets and shadowed sets. We use game theoretic approaches to find tradeoff solutions under competitive situations. This thesis uses the Gini coefficient to evaluate the impurities of regions in prob- abilistic rough sets. Three Gini objective functions are formulated in order to derive the optimal probabilistic thresholds. Then game theoretical approaches are applied in probabilistic rough sets to determine a pair of thresholds by finding a tradeoff between the region impurities. i The quantitative measures are used to evaluate the inclusion degree of an equiva- lence class in a concept when constructing three-way decisions with quantitative rough sets. The thesis applies game theoretic approaches in quantitative rough sets to for- mulate a game for each equivalence class. The game result representing a tradeo between measures determines a region that each equivalence class belongs to. Three-way approximations of shadowed sets map the membership grades of ob- jects into a three-value set. The errors are produced during the mapping. This thesis proposes the game-theoretic shadowed sets which utilize game theoretic approaches in shadowed sets to obtain the thresholds of three-way approximations. GTSS formu- lates the games between the elevation and reduction errors. The resulting thresholds induced from the game equilibria represent a tradeoff between these errors. The game theoretic approaches presented provide a semantically meaningful way to determine three-way decisions from a tradeoff perspective. The illustrative exam- ples are used to show the feasibility of the proposed approaches.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".