Revisiting the Performance of Weighted k-Nearest Centroid Neighbor Classifiers.
Bibliographic record
Abstract
k-Nearest Neighbor (KNN) is one of the most fundamental classification techniques. KNN relies on the distances of the samples to select k neighbors. k-Nearest Centroid Neighbor classification (KNCN) scheme, on the other hand, takes into account both the distances and the distribution of samples to improve the performance of KNN. In the past studies, with the help of two kernel functions, it was shown that assigning weights to the neighbors in KNCN further improve the performances of KNN based algorithms. In this study, we revisit the performance of Weighted k-Nearest Centroid Neighbor (WKNCN) method with various voting schemes and perform extensive comparison with other state-of-the-art KNN based algorithms. Unlike the previous studies, our experimental results show that weighted voting does not have any significant impact on the performance of KNCN method. To validate our claim, we design a new kernel for the WKNCN and perform statistical test on the experimental results. Our analysis with the various kernels also show that only well-designed distance based kernels like Inverse-distance kernel can exhibit comparable performance as the existing rank based kernels.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| 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".