CFAR++: Enhancing Rule Based Classifier
Bibliographic record
Abstract
Over the last few years, associative classifiers have shown massive success in mining patterns using association rules. These rule-based classifiers offer a level of human interpretability, addressing a common concern stemming from several deep learning models. Various associative classifiers have been proposed over the past that have shown state-of-the-art performance. However, those classifiers suffer the limitation of requiring parametric values which vary across different datasets. Furthermore, those frameworks do not consider the statistical significance of the rules. Recently, some works have addressed this limitation by proposing an associative classifier that incorporates the idea of using statistical significance to mine association classification rules. Though the recent associative classifiers show good performance, their performance is greatly affected by the dimension of the data. In this study, we explore the weakness of the recent associative classification models and experiment with using ensemble models to overcome such limitations, particularly on aggregating the ensemble models in a concise but effective predictor. We use 10 UCI datasets for evaluation of our new approach. From our study, we find the results based on the ensemble model with a delayed pruning are very competitive and can better handle large dimensional data spaces.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".