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
Associative classifiers have shown competitive performance with state-of-the-art methods for predicting class labels. In addition to accuracy performance, associative classifiers produce human readable rules for classification which provides an easier way to understand the decision process of the model. Early models of associative classifiers suffered from the limitation of selecting proper threshold values which are dataset specific. Recent work on associative classifiers eliminates that restriction by searching for statistically significant rules. However, a high dimensional feature vector in the training data impacts the performance of the model. Ensemble models like Random Forest are also very powerful tools for classification but the decision process of Random Forest is not easily understandable like the associative classifiers. In this study we propose Dynamic Ensemble Associative Learning (DEAL) where we use associative classifiers as base learners on feature sub-spaces. In our approach we select a subset of the feature vector to train each of the base learners. Instead of a random selection, we propose a dynamic feature sampling procedure which automatically defines the number of base learners and ensures diversity and completeness among the subset of feature vectors. We use 10 datasets from the UCI repository and evaluate the performance of the model in terms of accuracy and memory requirement. Our ensemble approach using the proposed sampling method largely decreases the memory requirement in the case of datasets having a large number of features and this without jeopardising accuracy. In fact, accuracy is also improved in most cases. Moreover, the decision process of our DEAL approach remains human interpretable by collecting and ranking the rules generated by the base learners predicting the final class label.
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 | 0.001 |
| 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