Delta-Convex Skins for Constructing Training and Testing sets in Supervised Learning
Bibliographic record
Abstract
The paper presents heuristics and several algorithms for constructing training/testing sets for supervised machine learning based on layered geometric structures <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\delta$</tex>-convex decompositions) extracted from input space data. Experiments with well-known datasets from public repositories showed that models derived from training/testing sets constructed via the proposed heuristics closely match or improve upon those using sets coming solely from random sampling. The insights coming from the exploitation of structural information represent a promising way of better using the available data for unsupervised and supervised modeling. Despite the advantages, the presented approaches have limitations and challenges associated with data complexities and geometric degeneracies usually related to the increased dimensionality of input feature spaces. The study discusses these issues and includes explorations of mitigation strategies based on working with lower dimensional manifold data representations with promising results. This is a preliminary study and future work exploring these concepts and approaches is necessary.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".