Exception-Tolerant Decision Tree / Rule Based Classifiers
Bibliographic record
Abstract
Many of the existing classifiers cannot deal with exceptions, which are not to be ignored in real life. In this paper, an exception-tolerant methodology is proposed based on each of the following three popular algorithms for multi-class classification problems: C4.5, PRISM and RISE. The performance of each algorithm was improved by converting the outputs into the format of RISE induced rules, applying bagging and boosting techniques, adding exceptions with a default rule, and excluding inefficient rules. The improved versions of C4.5, PRISM and RISE are named as OE 2 -C4.5, OE 2 -PRISM and OE 2 -RISE, respectively. Note that OE 2 stands for Ordering of Efficient Rules and Inclusion of Exceptions. Empirical results show that OE 2 -C4.5 and OE 2 -RISE significantly outperformed classical C4.5, PRISM and RISE for each dataset. Our methodology provides a reference for improving other weak learners individually or in an ensemble.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.010 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.007 |
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; both teacher heads agree on what is shown here.
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".