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 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".