Combining Binary Classifiers for a Multiclass Problem with Differential Privacy
Bibliographic record
Abstract
Multiclass classification problem is often solved by combing binary classifiers into ensembles. While this is required for inherently binary classifiers, such as SVM, it also provides performance advantages for other classifiers. In this paper, we address the problem of combining binary classifiers into ensembles in the differentially private data publishing framework, where the data privacy is achieved by anonymization. The main idea of this paper is to counter the inevitable loss of data quality due to anonymization of the data by building an ensemble of binary classifiers, and then to use an error-correcting approach to obtain a class decision from this ensemble. We describe the proposed algorithm and present the results of extensive experimentation on synthetic and UC Irvine data. We find that while building ensembles after anonymization leads to no change in classifier accuracy, preparing the data for ensembles prior to anonymization improves accuracy in most of the cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.040 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| 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 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".