Data fusion for pattern classification via the Dempster-Shafer evidence theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a novel technique to fuse multi information sources for the purpose of pattern classification. The proposed data fusion technique is based on the Dempster-Shafer evidence theory. Mass functions are derived from probabilistic and fuzzy measures that are associated with discriminant functions for pattern classification. Simulated synthetic images as well as real human brain magnetic resonance images (MRI) are tested to demonstrate the performance and effectiveness of the proposed approach. It is concluded from the experimental results that the proposed algorithm is quite effective and superior to other approaches such as the Bayesian approach. Furthermore, the paper explains how this approach exhibits a capability to handle uncertainty, imprecision and conflicts which often hinders multi information fusion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it