Aggregating predictions vs. aggregating features for relational classification

Abstract

Relational data classification is the problem of predicting a class label of a target entity given information about features of the entity, of the related entities, or neighbors, and of the links. This paper compares two fundamental approaches to relational classification: aggregating the features of entities related to a target instance, or aggregating the probabilistic predictions based on the features of each entity related to the target instance. Our experiments compare different relational classifiers on sports, financial, and movie data. We examine the strengths and weaknesses of both score and feature aggregation, both conceptually and empirically. The performance of a single aggregate operator (e.g., average) can vary widely across datasets, for both feature and score aggregation. Aggregate features can be adapted to a dataset by learning with a set of aggregate features. Used adaptively, aggregate features outperformed learning with a single fixed score aggregation operator. Since score aggregation is usually applied with a single fixed operator, this finding raises the challenge of adapting score aggregation to specific datasets.