Revisiting the Performance of Weighted k-Nearest Centroid Neighbor Classifiers.
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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