A Data-Fusion Scheme for Quantitative Image Analysis by Using Locally Weighted Regression and Dempster–Shafer Theory
Bibliographic record
Abstract
Dempster-Shafer (DS) theory provides a solution to fuse multisensor data, which are presented in a hypothesis space comprising mutually exclusive and exhaustive propositions and their unions. The fusion result is a description of the proposition with the values of support, plausibility, and uncertainty interval. However, in some applications, numerical values of a continuous function, instead of a Boolean value or a proposition, are expected. In this paper, a scheme based on DS reasoning and locally weighted regression is proposed to fuse the data obtained from the nondestructive inspections of aircraft lap joints for the estimation of the remaining thickness. The proposed approach uses a pairwise regression that is optimized by the DS method when multiple inputs are involved. The scheme is evaluated with the experiments on fusing conventional eddy current and pulsed eddy current data obtained from aircraft lap joint structures for the characterization of hidden corrosion.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".