Research map / Learning theory and algorithms / Prediction and decision losses
Adversarial Surrogate Losses for Ordinal Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| multiclass representation (ALord-mc) vs thresholded regression (ALord-th) | adversarial ordinal surrogate · UCI ordinal benchmark, large datasets (bank, calhousing) | MAE | Mixed |
| Gaussian kernel with ALord-th instead of linear SVORIM/SVOREX | thresholded adversarial surrogate · UCI ordinal benchmark, 8 datasets | average MAE: 0.531 vs 0.574/0.566 | Helped |
| ALord-mc multiclass surrogate vs cost-sensitive baselines | multiclass-based models · UCI ordinal benchmark, 12 datasets | average MAE: 0.613 vs 0.652 (CSOVO) and 0.797 (CSOVA) | Helped |
| ALord-th thresholded surrogate vs REDth reduction | threshold-based models · UCI ordinal benchmark, 12 datasets | average MAE: 0.626 vs 0.633 | Helped |
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