A K-NN associated fuzzy evidential reasoning classifier with adaptive neighbor selection
Bibliographic record
Abstract
We present a fuzzy evidential reasoning algorithm in light of the Dempster-Shafer evidence theory and the K-nearest neighbor algorithm for pattern classification. Given an input pattern to be classified, each of its K nearest neighbors is viewed as an evidence source, in terms of a fuzzy evidence structure. The distance between the input pattern and each of its K nearest neighbors is used for mass determination while the contextual information of the nearest neighbor in the training sample space is formulated by a fuzzy set in determining a fuzzy focal element. Therefore, pooling evidence provided by neighbors is realized by a fuzzy evidential reasoning, where feature selection is further considered through ranking and adaptive combination of neighbors. A fast implementation scheme of the fuzzy evidential reasoning is also developed. Experimental results of classifying multichannel remote sensing images have shown that the proposed approach outperforms the K-nearest neighbor (K-NN) algorithm [T.M. Cover et al. (1967)], the fuzzy K-nearest neighbor (F-KNN) algorithm [J.M. Keller et al. (1985)], the evidence-theoretic K-nearest neighbor (E-KNN) algorithm [T. Denoex (1995)], and the fuzzy extended version of E-KNN (FE-KNN) [L.M. Zouhal et al. (1997)], in terms of the classification accuracy and insensitivity to the number K of nearest neighbors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".