An adaptive fuzzy evidential nearest neighbor formulation for classifying remote sensing images
Bibliographic record
Abstract
The paper presents a novel adaptive fuzzy evidential nearest neighbor formulation for classifying remotely sensed images. The formulation combines the generalized fuzzy version of the Dempster-Shafer evidence theory (DSET) and the K-nearest neighbor (KNN) algorithm. Each of the K nearest neighbors provides evidence on the belongingness of the input pattern to be classified, and it is evaluated based on a measure of disapproval to achieve the adaptive capability during the classification process. The disapproval measure quantifies the lack of support with respect to the belongingness of the input pattern to a given class. Pieces of evidence are ranked based on their degree of disapproval and fused in a sequential manner. The pignistic Shannon entropy is used to estimate the degree of consensus among pieces of evidence provided by nearest neighbors and as a criterion for terminating the evidence fusion process. The paper reports the results of experimental work conducted to evaluate the proposed classification scheme using real multichannel remote sensing images. As will be demonstrated using the experimental results, the proposed classification scheme demonstrated robust performance and outperformed commonly used methods such as the K-nearest neighbor algorithm of Cover and Hart (1967), the fuzzy K-nearest neighbor algorithm of Keller et al. (1985), the evidence-theoretic K-nearest neighbor algorithm of Denoex (1995), and its fuzzy version of Zouhal and Denoex (1997). The performance of these techniques is examined with respect to the K-parameter and classification accuracy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".