Learning Bayesian Networks with Incomplete Data by Augmentation
Authors: Tameem Adel, Cassio de Campos
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform a wide range of experiments to demonstrate the benefits of learning Bayesian networks with such new approach. |
| Researcher Affiliation | Academia | Tameem Adel University of Manchester, UK tameem.hesham@gmail.com Cassio P. de Campos Queen s University Belfast, UK c.decampos@qub.ac.uk |
| Pseudocode | No | The paper describes the algorithms in prose but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions external tools and their code availability (e.g., 'Gobnilp...with the code available from https://www.cs.york.ac.uk/aig/sw/gobnilp/' and 'structural EM...implementation available at https://github.com/cassiopc/csda-dataimputation'), but it does not provide a link or statement about the open-sourcing of the authors' own implementation described in the paper. |
| Open Datasets | Yes | We perform experiments on simulated as well as real-world data. First, we employ the original Bayesian network model for Breast Cancer (Almeida et al. 2014)... Second, we use the Bayesian network that has been learned from the Prostate Cancer data... (Sarabando 2011; Almeida et al. 2014)... Third, the well-known ASIA network is used (Lauritzen and Speigelhalter 1988). The LUCAS dataset contains data of the LUCAS causal Bayesian network (Fogelman-Soulie 2008)... The Single Proton Emission Computed Tomography (SPECT) dataset consists of binary data denoting partial diagnosis from SPECT images (Lichman 2013). The dataset used in this experiment is taken from a smoking cessation study as described in Gruder et al. (1993). The Car Evaluation dataset (Blake and Merz 1998; Lichman 2013). |
| Dataset Splits | Yes | The evaluation metric is the accuracy of the test instances using a cross-validation approach, as usually done in classification experiments. ... (100-fold cross-validation in the MNAR setting case)... |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Gobnilp' and 'WEKA', but it does not specify their version numbers, which is required for reproducibility. |
| Experiment Setup | Yes | We use maximum number of parents, k = 3, and use t = 1. |