Research map / Label-efficient and robust learning / Semi-supervised learning
SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| dynamic chunking via change detection on classifier confidence (SAND-D) vs fixed chunks | ECSMiner · PAMAP | Error%: 35.26 -> 4.26 | Helped |
| SAND-D vs Adwin-based OBA | OBA · HyperPlane | Error%: 48.55 -> 0.01 | Helped |
| probabilistic CDT execution (SAND-D) instead of threshold-triggered (SAND-F) (ablation) | SAND · SynRBF@0.002 | Error%: 20.035 -> 32.13 | Hurt |
| SAND-D instead of SAND-F (ablation) | SAND · ForestCover | Error%: 6.445 -> 3.75 | Helped |
| reduce labeled data by lowering τ from 0.9 to 0.4 (ablation) | SAND-D · SynRBF@0.002 | Error%: 32.13 -> 54.4 | Hurt |
| reduce labeled data by lowering τ from 0.9 to 0.4 (ablation) | SAND-D · ForestCover | Error%: 3.75 -> 4.69 | Hurt |
| SAND-D novel class detection vs ECSMiner | ECSMiner · PAMAP | Fnew: 37.530 -> 3.884 | Helped |
| SAND-D novel class detection vs ECSMiner | ECSMiner · ForestCover | Mnew: 8.417 vs 8.525 | No clear effect |
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