CS3W-GBG: A Cost-Sensitive Three-Way Granular-Ball Generation Method
Bibliographic record
Abstract
As an innovative methodology in data processing and knowledge representation, granular-ball computing (GBC) adaptively generates distinct neighborhoods for individual objects, thereby improving both generality and flexibility. By replacing point inputs with granular-balls (GBs), GBC achieves substantial efficiency gains. However, traditional GB-based classifiers may produce unreliable classifications under uncertain conditions. To address this limitation, we propose a novel approach that integrates three-way decision (3WD) theory with GBC, enabling robust handling of uncertain classification problems. This study first introduces a sequential three-way decision with fuzzy granular-ball rough sets (S3WD-FGBRS). We systematically analyze the changing rules of the multilevel decision cost in S3WD-FGBRS and its three regions. Building upon the principle of justifiable granularity, we develop a cost-sensitive three-way granular-ball generation method (CS3W-GBG) based on S3WD-FGBRS that incorporates a granularity optimization mechanism. To validate our approach, we conduct comprehensive experiments using three state-of-the-art GB classifiers and two benchmark classifiers on 12 publicly available datasets. Experimental results demonstrate that CS3W-GBG exhibits strong resilience in processing uncertain data through its 3WD strategy. Furthermore, our method achieves competitive performance compared to existing approaches in terms of classification accuracy and robustness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".