A Proposed Framework for Incorporating `Weight of Evidence' Within A Fuzzy Rule Based Classification System
Bibliographic record
Abstract
Two different strategies for the firing and weighting of rules in a fuzzy rule based classification system (FRBCS) are examined. The rules are automatically obtained by the "pattern discovery" (PD) by A.K.C. Wong and Y. Wang (1997) algorithm and applied via input membership functions to a FRBCS by A. Hamilton-Wright and D. W. Stashuk (2005). Classification performance of the FRBCS when using an occurrence based rule weighting and standard rule firing logic was compared to performance obtained when using a weight of evidence (WOE) based rule weighting with an "independent" rule firing strategy. Performance was studied across various linearly and non-linearly separable class distributions. A new defuzzification and rule firing framework required to accommodate the WOE rule weighting is described. The occurrence based rule weighting and standard rule firing strategy performed better than WOE rule weighting and "independent" rule firing. Variance in the estimation of the WOE statistic, related to the number of training examples available, may limit the performance of the WOE scheme.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".