A survey on Bayesian network structure learning from data

A necessary step in the development of artificial intelligence is to enable a machine to represent how the world works, building an internal structure from data. This structure should hold a good trade-off between expressive power and querying efficiency. Bayesian networks have proven to be an effec...

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Veröffentlicht in:Progress in artificial intelligence Jg. 8; H. 4; S. 425 - 439
Hauptverfasser: Scanagatta, Mauro, Salmerón, Antonio, Stella, Fabio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Bayesian analysis
Bioinformatics
Computational Intelligence
Computer Imaging
Computer Science
Continuity (mathematics)
Control
Data Mining and Knowledge Discovery
Dependence
Economic analysis
Image processing
Machine learning
Mechatronics
Missing data
Natural Language Processing (NLP)
Pattern Recognition and Graphics
Power efficiency
Review
Risk analysis
Robotics
Software
Software development tools
Vision
Structure learning
Statistics
Bayesian network
Machine learning
ISSN:2192-6352, 2192-6360
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Zusammenfassung:A necessary step in the development of artificial intelligence is to enable a machine to represent how the world works, building an internal structure from data. This structure should hold a good trade-off between expressive power and querying efficiency. Bayesian networks have proven to be an effective and versatile tool for the task at hand. They have been applied to modeling knowledge in a variety of fields, ranging from bioinformatics to law, from image processing to economic risk analysis. A crucial aspect is learning the dependency graph of a Bayesian network from data. This task, called structure learning , is NP-hard and is the subject of intense, cutting-edge research. In short, it can be thought of as choosing one graph over the many candidates, grounding our reasoning over a collection of samples of the distribution generating the data. The number of possible graphs increases very quickly at the increase in the number of variables. Searching in this space, and selecting a graph over the others, becomes quickly burdensome. In this survey, we review the most relevant structure learning algorithms that have been proposed in the literature. We classify them according to the approach they follow for solving the problem and we also show alternatives for handling missing data and continuous variable. An extensive review of existing software tools is also given.
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ISSN:2192-6352
2192-6360
DOI:10.1007/s13748-019-00194-y