A scheme for constructing evidence structures in Dempster-Shafer evidence theory for data fusion
Bibliographic record
Abstract
This paper addresses the issue of evidence structure construction involved in the Dempster-Shafer evidence theory (DSET) based reasoning for data fusion. An in-depth study is carried out on the properties of the proposed Proportional Difference Evidence Structure Constructing Scheme (PDESCS). Some properties have been mathematically proved for the PDESCS associated DSET. If PDESCS is applied to probabilistic evidence, in terms of posterior probability distributions, the DSET based reasoning with the maximum commonality decision making scheme is equivalent to the Beyesian approach with the maximum a posteriori probability principle (MAP). If PDESCS is applied to fuzzy evidence, in terms of fuzzy sets, the DSET based reasoning is equivalent to applying the maximum fuzzy membership decision making scheme to the intersected fuzzy set by the product T-norm operator. If PDESCS is applied to both probabilistic evidence and fuzzy evidence, the DSET based reasoning is equivalent to applying the maximum fuzzy set probability decision making scheme. To show the effectiveness of the PDESCS associated DSET, experiments are carried out for classifying human brain MR (magnetic resonance) images. It is concluded that the proposed scheme works well, and provides not only a unified framework to accommodate probabilistic evidence and fuzzy evidence, but also an effective reasoning mechanism to deal with different uncertainty, in terms of randomness and fuzziness, as well as precision.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".