Gaussian Logic for Predictive Classification
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Kuželka, Ondřej
Szabóová, Andrea
Holec, Matěj
Železný, Filip
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We describe a statistical relational learning framework called
Gaussian Logic capable to work efficiently with combinations of relational
and numerical data. The framework assumes that, for a fixed relational
structure, the numerical data can be modelled by a multivariate
normal distribution. We demonstrate how the Gaussian Logic framework
can be applied to predictive classification problems. In experiments, we
first show an application of the framework for the prediction of DNAbinding
propensity of proteins. Next, we show how the Gaussian Logic
framework can be used to find motifs describing highly correlated gene
groups in gene-expression data which are then used in a set-level-based
classification method.
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