Research map / Label-efficient and robust learning / Recognition and long-tail
Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Bayesian-network class ordering and parent-only conditioning (RBCC vs RCC) | recurrent classifier chain · Scene | Subset accuracy: 0.548 ± 0.006 -> 0.653 ± 0.007 | Helped |
| RBCC vs RCC | recurrent classifier chain · PASCAL VOC 2007 | Subset accuracy: 0.457 ± 0.057 -> 0.594 ± 0.009 | Helped |
| Bayesian-network ordering applied with topological sort in RCC (TS-RCC) instead of frequency order | RCC · Yelp | Subset accuracy: 0.212 ± 0.002 -> 0.192 ± 0.010 | Hurt |
| RBCC vs OF-RCC on Macro-F1 | RBCC · Scene | Macro-F1: 0.742 -> 0.727 | Hurt |
| hill climbing vs other Bayesian network learning methods (ablation) | RBCC · Scene | Subset accuracy: roughly 0.5% | No clear effect |
| Chow-Liu network (fewer parents) instead of hill climbing (ablation) | RBCC · Scene | Subset accuracy / Hamming loss | Mixed |
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