Optimizing Binary Decision Diagrams with MaxSAT for Classification
Authors: Hao Hu, Marie-José Huguet, Mohamed Siala3767-3775
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical study shows clear benefits of the proposed approach in terms of prediction quality and interpretability (i.e., lighter size) compared to the state-of-the-art approaches. |
| Researcher Affiliation | Academia | LAAS-CNRS, Universit e de Toulouse, CNRS, INSA, Toulouse, France |
| Pseudocode | No | The paper describes algorithmic steps in prose but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The source code and the datasets are available online at https://gitlab.laas.fr/hhu/bddencoding |
| Open Datasets | Yes | We consider datasets from CP4IM 3. These datasets are binarized with the one-hot encoding. Footnote 3: https://dtai.cs.kuleuven.be/CP4IM/datasets/ |
| Dataset Splits | Yes | For each dataset, we use random 5-fold cross-validation with 5 different seeds. |
| Hardware Specification | Yes | All experiments were run on a cluster using Xeon E5-2695 v3@2.30GHz CPU and running x Ubuntu 16.04.6 LTS. |
| Software Dependencies | No | The Max SAT solver we used is Loandra (Berg, Demirovi c, and Stuckey 2019), an efficient incomplete Max SAT solver that return the best solution found within a limited computation time or report optimality. While the solver is named, a specific version number for it or other key software components is not provided. |
| Experiment Setup | Yes | We consider the P bdd(E, H) problem with 5 different depths H {2, 3, 4, 5, 6}. For each experiment, the time limit for generating formulas and the time limit for solver are set to 15 minutes. |