Published February 27, 2023 | Version v1
Dataset Open

Bayesys datasets

Description

Various datasets from the Bayesys repository.

Size: 6 groups of datasets with each up to 16 experimentally generated from the bayesian network with the number of observation 100,1000,…100000. Ground truth is given

Number of features: 6 - over 1000

Ground truth: Yes

Type of Graph: Directed graph

 

Six discrete BN case studies are used to generate data. The first three of them represent well-established examples from the BN structure learning literature, whereas the other three represent new cases and are based on recent BN real-world applications. Specifically,

  • Asia: A small toy network for diagnosing patients at a clinic;

  • Alarm: A medium-sized network based on an alarm message system for patient monitoring;

  • Pathfinder: A very large network that was designed to assist surgical pathologists with the diagnosis of lymph-node diseases;

  • Sports: A small BN that combines football team ratings with various team performance statistics to predict a series of match outcomes;

  • ForMed: A large BN that captures the risk of violent reoffending of mentally ill prisoners, along with multiple interventions for managing this risk;

  • Property: A medium BN that assesses investment decisions in the UK property market.

Data generated with noise:

Synthetic datasets - noise
Experiment No. Experiment Notes
1 N No noise
2 M5 Missing data (5%)
3 M10 Missing data (10%)
4 I5 Incorrect data (5%)
5 I10 Incorrect data (10%)
6 S5 Merged states data (5%)
7 S10 Merged states data (10%)
8 L5 Latent confounders (5%)
9 L10 Latent confounders (10%)
10 cMI M5 and I5
11 cMS M5 and S5
12 cML M5 and L5
13 cIS I5 and S5
14 cIL I5 and L5
15 cSL S5 and L5
16 cMISL M5, I5, S5 and L5

More information about the datasets is contained in the dataset_description.html files.

Files

bayesys_datasets.zip

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Additional details

Related works

Is derived from
http://constantinou.info/downloads/bayesys/bayesys_repository.pdf (URL)

References

  • A.C. Constantinou, Y. Liu, K. Chobtham, Z. Guo, N.K. Kitson The Bayesys Data and Bayesian Network Repository Queen Mary University of London, London, UK (2020) [Online]. Available: http://constantinou.info/downloads/bayesys/bayesys_repository.pdf
  • Constantinou, A. C., Liu, Y., Chobtham, K., Guo, Z., and Kitson, N. K. (2021). Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data. International Journal of Approximate Reasoning, Vol. 131, pp. 151-188. DOI: https://doi.org/10.1016/j.ijar.2021.01.001