Name
Abstract | ||
|---|---|---|
A relational dependency network (RDN) is a directed graphical model widely used for multi-relational data. These networks allow cyclic dependencies, necessary to represent relational auto-correlations. We describe an approach for learning both the RDN’s structure and its parameters, given an input relational database: First learn a Bayesian network (BN), then transform the Bayesian network to an RDN. Thus fast Bayesian network learning translates into fast RDN learning. The BN-to-RDN transform comprises a simple, local adjustment of the Bayesian network structure and a closed-form transform of the Bayesian network parameters. This method can learn an RDN for a dataset with a million tuples in minutes. We empirically compare our approach to a state-of-the-art RDN learning approach that applies functional gradient boosting, using six benchmark datasets. Learning RDNs via BNs scales much better to large datasets than learning RDNs with current boosting methods. |
Year | DOI | Venue |
|---|---|---|
2014 | https://doi.org/10.1007/s10994-016-5557-9 | Machine Learning |
Keywords | Field | DocType |
Bayesian Network,Target Node,Descriptive Attribute,Dependency Network,Bayesian Network Structure | Data mining,Relational database,Pattern recognition,Computer science,Tuple,Dependency network,Bayesian network,Artificial intelligence,Boosting (machine learning),Graphical model,Machine learning,Gradient boosting | Journal |
Volume | Issue | ISSN |
abs/1410.7835 | 3 | 0885-6125 |
Citations | PageRank | References |
1 | 0.35 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oliver Schulte | 1 | 134 | 25.15 |
| Zhensong Qian | 2 | 1 | 0.35 |
| Arthur E. Kirkpatrick | 3 | 1 | 0.35 |
| Xiaoqian Yin | 4 | 1 | 0.35 |
| Yan Lindsay Sun | 5 | 75 | 10.41 |