One-Class versus Binary Classification: Which and When?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Binary classifiers have typically been the norm for building classification models in the Machine Learning community. However, an alternate to binary classification is one-class classification, which aims to build models using only a single class of data. This is particularly useful when there is an over-abundance of data of a particular class. In such imbalanced cases, binary classifiers may not perform very well, and one-class classifiers then become the viable option. In this paper, we are interested in investigating the performance of binary and one-class classifiers as the level of imbalance increases, and, thus, uncertainty in the second class. Our objective is to gain insight into which classification paradigm becomes more suitable as imbalance and uncertainty increase. To this end, we conduct experiments on various datasets, both artificial and from the UCI repository, and monitor the performance of the binary and one-class classifiers as the size of the second class gradually decreases, thus increasing the level of imbalance. The results show that as the level of imbalance increases, the performance of binary classifiers decreases, whereas one-class classifiers stay relatively stable.
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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 | 0.002 |
| Open science | 0.001 | 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