document: Aggregation-based feature invention and relational concept classes Model induction from relational data requires aggregation of the values of attributes of related entities . This paper makes three contributions to the study of relational learning . ( 1 ) It presents a hierarchy of relational concepts of increasing complexity , using relational schema characteristics such as cardinality , and derives classes of aggregation operators that are needed to learn these concepts . ( 2 ) Expanding one level of the hierarchy , it introduces new aggregation operators that model the distributions of the values to be aggregated and ( for classification problems ) the differences in these distributions by class . ( 3 ) It demonstrates empirically on a noisy business domain that more-complex aggregation methods can increase generalization performance . Constructing features using target-dependent aggregations can transform relational prediction tasks so that well-understood feature-vector-based modeling algorithms can be applied successfully .
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