Research map / Label-efficient and robust learning / Recognition and long-tail
Multi-Dimensional Classification via kNN Feature Augmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add kNN-augmented class-count features (KRAM) to BR | BR with SVM/NB · 11 MDC data sets, 3 metrics | win/tie/loss: 29/36/1 | Helped |
| add kNN-augmented features to ECC | ECC with SVM/NB · 11 MDC data sets, 3 metrics | win/tie/loss: 31/35/0 | Helped |
| add kNN-augmented features to ECP | ECP with SVM/NB · 11 MDC data sets, 3 metrics | win/tie/loss: 26/29/11 | Mixed |
| add kNN-augmented features to ESC | ESC with SVM/NB · 11 MDC data sets, 3 metrics | win/tie/loss: 28/36/2 | Helped |
| add kNN features to BR | BR + SVM · Scene | exact match: .530 -> .651 | Helped |
| add kNN features to BR | BR + NB · WQplants | hamming score: .397 -> .506 | Helped |
| add kNN features to ECP | ECP + SVM · WQplants | exact match: .093 -> .067 | Hurt |
| add kNN features to ECP | ECP + NB · Thyroid | exact match: .793 -> .768 | Hurt |
| add kNN features to BR | BR + SVM · Flare1 | exact match: .821 -> .818 | No clear effect |
| vary k from 5 to 10 (ablation) | KRAM-BR · MDC data sets | all metrics | No clear effect |
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