The application of Rough Sets Theory as a data-mining tool to classify complex functions in safety management
Bibliographic record
Abstract
In recent years, considerable research efforts in safety manage-ment were directed at proposing innovative methodological frameworks to address the complexity of modern sociotechnical systems. The significance of results in such endeavors, whether quantitative or qualitative, relies largely on the quality of input data and the validity of the implemented meth-ods to model such systems. To provide more objective and valid results, new protocols and tools for data processing are needed as well. An inter-esting data-mining tool for computing with incomplete and uncertain infor-mation is Rough Set Theory (RST). In this study, we propose the application of RST to generate comprehensible IF-THEN rule bases for classifying out-comes within the framework of the Functional Resonance Analysis Method (FRAM). The steps for the integration process of both frameworks are intro-duced in this paper and an illustrative example is consequently provided to demonstrate a possible approach for realizing the combination. Such an ap-proach could allow for an efficient rule generation and data classification process, which could aid in addressing classification challenges and input data limitations in safety management. The model however still requires fur-ther optimization and validation using expert’s input data in future applica-tions.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".