bnlearn datasets
Description
A collection of various structure learning datasets from the Bayesian Network Repository with description files.
Size: 5 simple datasets
Number of features: 3 - 56
Ground truth: Yes
Type of Graph: Directed Graph
The alarm dataset contains the following 37 variables:
CVP (central venous pressure): a three-level factor with levels LOW, NORMAL and HIGH.
PCWP (pulmonary capillary wedge pressure): a three-level factor with levels LOW, NORMAL and HIGH.
HIST (history): a two-level factor with levels TRUE and FALSE.
TPR (total peripheral resistance): a three-level factor with levels LOW, NORMAL and HIGH.
... (33 more variables, see the corresponding .html file)
The asia dataset contains the following variables:
D (dyspnoea), a two-level factor with levels yes and no.
T (tuberculosis), a two-level factor with levels yes and no.
L (lung cancer), a two-level factor with levels yes and no.
B (bronchitis), a two-level factor with levels yes and no.
A(visit to Asia), a two-level factor with levels yes and no.
S (smoking), a two-level factor with levels yes and no.
X (chest X-ray), a two-level factor with levels yes and no.
E (tuberculosis versus lung cancer/bronchitis), a two-level factor with levels yes and no.
The coronary dataset contains the following 6 variables:
Smoking (smoking): a two-level factor with levels no and yes.
M. Work (strenuous mental work): a two-level factor with levels no and yes.
P. Work (strenuous physical work): a two-level factor with levels no and yes.
Pressure (systolic blood pressure): a two-level factor with levels <140 and >140.
Proteins (ratio of beta and alpha lipoproteins): a two-level factor with levels <3 and >3.
Family (family anamnesis of coronary heart disease): a two-level factor with levels neg and pos.
The hailfinder dataset contains the following 56 variables:
N07muVerMo (10.7mu vertical motion): a four-level factor with levels StrongUp, WeakUp, Neutral and Down.
SubjVertMo (subjective judgment of vertical motion): a four-level factor with levels StrongUp, WeakUp, Neutral and Down.
QGVertMotion (quasigeostrophic vertical motion): a four-level factor with levels StrongUp, WeakUp, Neutral and Down.
CombVerMo (combined vertical motion): a four-level factor with levels StrongUp, WeakUp, Neutral and Down.
AreaMesoALS (area of meso-alpha): a four-level factor with levels StrongUp, WeakUp, Neutral and Down.
SatContMoist (satellite contribution to moisture): a four-level factor with levels VeryWet, Wet, Neutral and Dry.
... (49 more variables are in the correspondent .html file)
The lizards dataset contains the following 3 variables:
Species (the species of the lizard): a two-level factor with levels Sagrei and Distichus.
Height (perch height): a two-level factor with levels high (greater than 4.75 feet) and low (lesser or equal to 4.75 feet).
Diameter (perch diameter): a two-level factor with levels narrow (greater than 4 inches) and wide (lesser or equal to 4 inches).
More information about the datasets is contained in the dataset_description.html files.
Files
bnlearn_data.zip
Files
(2.1 MB)
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md5:f123ea701227cfd8a43996183b7c5279
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Additional details
Identifiers
Related works
- Is documented by
- Book chapter: 10.1007/978-1-4757-3502-4_6 (DOI)
References
- Elidan, G. Bayesian Network Repository. (2001), https://www.cs.huji.ac.il/w~galel/Repository/
- Beinlich I, Suermondt HJ, Chavez RM, Cooper GF (1989). "The ALARM Monitoring System: A Case Study with Two Probabilistic Inference Techniques for Belief Networks". Proceedings of the 2nd European Conference on Artificial Intelligence in Medicine, 247–256.
- Scutari M (2010). "Learning Bayesian Networks with the bnlearn R Package." Journal of Statistical Software, 35(3), 1–22. doi:10.18637/jss.v035.i03