Associative Classification with Statistically Significant Positive and Negative Rules
Why this work is in the frame
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Bibliographic record
Abstract
Rule-based classifier has shown its popularity in building many decision support systems such as medical diagnosis and financial fraud detection. One major advantage is that the models are human understandable and can be edited. Associative classifiers, as an extension of rule-based classifiers, use association rules to associate attributes with class labels. A delicate issue of associative classifiers is the need for subtle thresholds: minimum support and minimum confidence. Without prior knowledge, it could be difficult to choose the proper thresholds, and the discovered rules within the support-confidence framework are not statistically significant, i.e., inclusion of noisy rules and exclusion of valuable rules. Besides, most associative classifiers proposed so far, are built with only positive association rules. Negative rules, however, are also able to provide valuable information to discriminate between classes. To solve the above mentioned problems, we propose a novel associative classifier which is built upon both positive and negative classification association rules that show statistically significant dependencies. Experimental results on real-world datasets show that our method achieves competitive or even better performance than well-known rule-based and associative classifiers in terms of both classification accuracy and computational efficiency.
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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