Predicting the Hardness of Learning Bayesian Networks
Authors: Brandon Malone, Kustaa Kangas, Matti Jarvisalo, Mikko Koivisto, Petri Myllymaki
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results, based on the largest evaluation of stateof-the-art BNS learning algorithms to date, demonstrate that we can predict the runtimes to a reasonable degree of accuracy, and effectively select algorithms that perform well on a particular instance. |
| Researcher Affiliation | Academia | Helsinki Institute for Information Technology & Department of Computer Science, University of Helsinki, Finland |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions links to external solvers used (GOBNILP, URLearning) and an 'online supplement' for 'more details' (http://bnportfolio.cs.helsinki.fi/), but does not explicitly state that the source code for their own methodology is being released or provide a clear link to it. |
| Open Datasets | Yes | Datasets originating from the UCI repository (Bache and Lichman 2013). 19 datasets. Datasets sampled from benchmark Bayesian networks, downloaded from http://www.cs.york.ac.uk/aig/ sw/gobnilp/. |
| Dataset Splits | Yes | We evaluated the portfolios using the standard 10-fold cross-validation technique. That is, the data is partitioned into 10 non-overlapping subsets. In each fold, 9 of the subsets are used to train the model, and the remaining set is used for testing; each subset is used as the testing set once. |
| Hardware Specification | Yes | For running the experiments we used a cluster of Dell Power Edge M610 computing nodes equipped with two 2.53GHz Intel Xeon E5540 CPUs and 32-GB RAM. |
| Software Dependencies | Yes | We use the GOBNILP solver, version 1.4.1 (http: //www.cs.york.ac.uk/aig/sw/gobnilp/) as a representative for ILP. GOBNILP uses the SCIP framework (http://scip.zib.de/) and an external linear program solver; we used SCIP 3.0.1 and So Plex 1.7.1 (http:// soplex.zib.de/). |
| Experiment Setup | Yes | For each dataset and scoring function, we generated scores with parent limits ranging from 2 to up to 6. For each individual run, we used a timeout of 2 hours and a 28-GB memory limit. We considered 5 different scoring functions: BDeu with the Equivalent Sample Size parameter selected from {0.1, 1, 10, 100} and the BIC scores. |