Research map / Learning theory and algorithms / Prediction and decision losses
Optimal Decision Diagrams for Classification
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use optimal decision diagrams instead of optimal decision trees with multivariate splits | ODD classification · UCI 54 datasets, test | accuracy: 0.799 -> 0.809 | Helped |
| Use optimal decision diagrams instead of optimal decision trees with univariate splits | ODD classification · UCI 54 datasets, test | accuracy: 0.795 -> 0.799 | No clear effect |
| Enforce minimum flow S=0.10n rather than S=0.05n (ablation) | ODD · Test datasets, multivariate splits | accuracy: 0.828 -> 0.827 | Hurt |
| Enforce minimum flow S=0.15n rather than S=0.05n (ablation) | ODD · Test datasets, multivariate splits | accuracy: 0.828 -> 0.823 | Hurt |
| Enforce minimum flow S=0.20n rather than S=0.05n (ablation) | ODD · Test datasets, multivariate splits | accuracy: 0.828 -> 0.823 | Hurt |
| Enforce minimum flow S=0.10n rather than S=0.05n (ablation) | ODD · Test datasets, univariate splits | accuracy: 0.840 -> 0.838 | Hurt |
| Enforce minimum flow S=0.15n rather than S=0.05n (ablation) | ODD · Test datasets, univariate splits | accuracy: 0.840 -> 0.837 | Hurt |
| Enforce minimum flow S=0.20n rather than S=0.05n (ablation) | ODD · Test datasets, univariate splits | accuracy: 0.840 -> 0.835 | Hurt |
Closest papers by meaning
- The Influence of Dimensions on the Complexity of Computing Decision Trees (AAAI 2023 · similarity 0.87)
- Learning Minimum-Size BDDs: Towards Efficient Exact Algorithms (ICML 2025 · similarity 0.86)
- Learning Small Decision Trees with Few Outliers: A Parameterized Perspective (AAAI 2024 · similarity 0.86)
- Witty: An Efficient Solver for Computing Minimum-Size Decision Trees (AAAI 2025 · similarity 0.85)
- Learning Small Decision Trees for Data of Low Rank-Width (AAAI 2024 · similarity 0.84)
- Optimal Decision Tree Pruning Revisited: Algorithms and Complexity (ICML 2025 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.