Abstract
Relational data offer a unique opportunity for improving
the classification accuracy of statistical models. If two
objects are related, inferring something about one object
can aid inferences about the other. We present an iterative
classification procedure that exploits this characteristic of
relational data. This approach uses simple Bayesian
classifiers in an iterative fashion, dynamically updating
the attributes of some objects as inferences are made about
related objects. Inferences made with high confidence in
initial iterations are fed back into the data and are used to
inform subsequent inferences about related objects. We
evaluate the performance of this approach on a binary
classification task. Experiments indicate that iterative
classification significantly increases accuracy when
compared to a single-pass approach.
