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.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".