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16

ECML
2007
Springer

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Structure Learning of Probabilistic Relational Models from Incomplete Relational Data

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Structure Learning of Probabilistic Relational Models from Incomplete Relational Data

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Abstract. Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn structures of probabilistic relational model (PRM) from incomplete relational data. The missing values are ﬁlled in randomly at ﬁrst, and a maximum likelihood tree (MLT) is generated from the complete data sample. Then, Gibbs sampling is combined with MLT to modify the data and regulate MLT iteratively for obtaining a well-completed data set. Finally, probabilistic structure is learned through dependency analysis from the completed data set. Experiments show that the MGDA approach can learn good structures from incomplete relational data.

Xiao-Lin Li, Zhi-Hua Zhou

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ECML 2007 | Incomplete Relational Data | Machine Learning | Probabilistic Relational Model | Relational Data |

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Added	07 Jun 2010
Updated	07 Jun 2010
Type	Conference
Year	2007
Where	ECML
Authors	Xiao-Lin Li, Zhi-Hua Zhou

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