Research map / Learning theory and algorithms / Combinatorial and search optimization
A Scalable Deterministic Global Optimization Algorithm for Training Optimal Decision Tree
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use RS-OCT instead of OCT | optimal decision tree training · small and medium datasets (n < 1000) | testing accuracy: 88.2 vs 86.3 on SEEDS; 62.5 vs 64.3 on GLASS | Mixed |
| use RS-OCT instead of OCT | optimal decision tree training · all 15 small and medium datasets | testing accuracy: +1.0% on average vs OCT | Helped |
| use RS-OCT instead of OCT | optimal decision tree training · medium datasets (n >= 1000) | testing accuracy: +1.4% on average vs OCT | Helped |
| use RS-OCT instead of OCT | optimal decision tree training · small and medium datasets | training accuracy: +0.8% on average vs OCT | Helped |
| use RS-OCT instead of OCT | optimal decision tree training · GLASS | optimality gap: 99.9 -> <1% | Helped |
| use RS-OCT instead of OCT | optimal decision tree training · BANKNOTE | optimality gap: 98.7 -> <1% | Helped |
| use RS-OCT instead of OCT | optimal decision tree training · WALL-FOLLOWING | optimality gap: 100 -> <1% | Helped |
| use RS-OCT instead of OCT | optimal decision tree training · large datasets (n >= 7,000) | upper bound: 11.7% improvement on average | Helped |
| use RS-OCT instead of OCT | optimal decision tree training · large datasets (n >= 7,000) | training accuracy: +3.6% on average vs OCT | Helped |
| use RS-OCT instead of OCT | optimal decision tree training · large datasets (n >= 7,000) | testing accuracy: +2.8% on average vs OCT | Helped |
| use RS-OCT instead of OCT | optimal decision tree training · SKIN-SEGMENTATION | upper bound: 23409.5 -> 16953.7 | Helped |
| use RS-OCT instead of OCT | optimal decision tree training · SPAMBASE | testing accuracy: 84.5 -> 85.9 | Helped |
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