Research map / Learning theory and algorithms / Submodular and game algorithms
Subset Selection by Pareto Optimization with Recombination
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add recombination (one-point or uniform) to mutation-only POSS (ablation) | POSS · Unsupervised feature selection, 10 datasets, k=8 | error ratio: average rank 3.05 -> 1.60 (one-point), 1.40 (uniform) | Helped |
| add recombination to POSS (ablation) | POSS · Sparse regression, 10 datasets, k=8 | R2: average rank 2.95 -> 1.85 (one-point), 1.25 (uniform) | Helped |
| add recombination to POSS (ablation) | POSS · smallNORB sparse regression | R2: 0.535 -> 0.547 (one-point), 0.550 (uniform) | Helped |
| add recombination to POSS (ablation) | POSS · mediamill feature selection | error ratio: 1.604 -> 1.579 (one-point), 1.559 (uniform) | Helped |
| add recombination to POSS (ablation) | POSS · phishing feature selection | error ratio: 1.168 -> 1.166 | No clear effect |
| add recombination to POSS (ablation) | POSS · cifar10 sparse regression | R2: 0.070 -> 0.071 | No clear effect |
| add recombination to POSS | POSS · Definition 5 synthetic objective | expected iterations to optimum: at least (n/(3e4ek))^k -> at most 6ek2n2 | Helped |
Closest papers by meaning
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