Top-down induction of first-order logical decision trees
Artificial Intelligence
Refining aggregate conditions in relational learning
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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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.