In relational learning, one learns patterns from relational databases, which usually contain multiple tables that are interconnected via relations. Thus, an example for which a prediction is to be given may be related to a set of objects that are possibly relevant for that prediction.
Relational classifiers differ with respect to how they handle these sets: some use
properties of the set as a whole (using aggregation), some refer to
properties of specific individuals, however, most classifiers do not combine both. This imposes an undesirable bias on these learners. This dissertation describes a learning approach that
avoids this bias, using complex aggregates, i.e., aggregates that impose selection conditions on the set to aggregate on.