Optimal Feature Selection for Decision Robustness in Bayesian Networks
Authors: YooJung Choi, Adnan Darwiche, Guy Van den Broeck
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on Naive Bayes, as well as more general networks, show the efficacy and distinct behavior of this decision-making approach. |
| Researcher Affiliation | Academia | Yoo Jung Choi, Adnan Darwiche, and Guy Van den Broeck Computer Science Department University of California, Los Angeles |
| Pseudocode | Yes | Algorithm 1 SDPd,T (X | Y, e) and Algorithm 2 FS-SDD(Q, d, b) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. It mentions a third-party tool, SAMIAM, but not its own code. |
| Open Datasets | Yes | We evaluated our system on Naive Bayes networks from the UCI repository [Bache and Lichman, 2013], BFC (http://www.berkeleyfreeclinic. org/), and CRESST (http://www.cse.ucla.edu/). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | a 2.6GHz Intel Xeon E5-2670 CPU with 4GB RAM was used. |
| Software Dependencies | No | The paper mentions using 'jointree inference as implemented in SAMIAM' but does not provide a specific version number for SAMIAM or any other software dependencies with versions. |
| Experiment Setup | Yes | For each network, we find the optimal subset for E-SDP with the budget set to 1/3 the number of features. In all experiments, the cost of each feature is 1, timeout is 1 hour, and a 2.6GHz Intel Xeon E5-2670 CPU with 4GB RAM was used. |