Avoiding Bias When Aggregating Relational Data With Degree Disparity

D. Jensen, J. Neville and M. Hay (2003). Avoiding bias when aggregating relational data with degree disparity. Proceedings of the 20th International Conference on Machine Learning.

Abstract
A common characteristic of relational data sets — degree disparity — can lead relational learning algorithms to discover misleading correlations. Degree disparity occurs when the frequency of a relation is correlated with the values of the target variable. In such cases, aggregation functions used by many relational learning algorithms will result in misleading correlations and added complexity in models. We examine this problem through a combination of simulations and experiments. We show how two novel hypothesis testing procedures can adjust for the effects ofusing aggregation functions in the presence of degree disparity.
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