Three-way decision with granular rough sets
Bibliographic record
Abstract
By integrating granular computing with rough set theory, granular rough sets enhance the semantics and effectiveness of decision-making through granule-based representations. Existing research has not thoroughly explored the issues of inducing three-way decision rules with granular rough sets, partly due to the challenge of meaningfully describing granules. To address these gaps, this paper proposes a unified framework for three-way decision models based on granular rough sets. Additionally, we introduce a generalized formulation for granule descriptions. It extends traditional representations to include all possible descriptions within a given domain. Through the lens of the proposed framework and granular descriptions, we formulate a three-way decision model in generalized granular rough sets and further demonstrate its instantiation potential across three specific types of granular spaces: quotient spaces, neighborhood-induced granular spaces, and maximal-clique-induced granular spaces. The effectiveness of the proposed models is illustrated through examples using set-valued information tables and experiments on real-world datasets. The results show that the proposed models have good performance and practical applicability. • A framework for three-way decision is proposed based on granular rough sets. • A new granule description is formulated to support general model building. • The framework is applied to three granular spaces with matching algorithms. • Experiments show improved accuracy and precision in the results.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".