Aggregation of Subclassifications: Methods, Tools and Experiments

摘要

Aggregation methods have been studied extensively from a mathematical, theoretical point of view. In this work, however, we focus on a more practical aspect: subclassifications. Given class predictions for several sub-objects of a single instance, we systematically investigate the performance of different aggregation methods. To this end, we simulate data for various data distributions. Thus we ensure that we know the ground truth for the evaluation, which would be impossible for real world data. Our source code is publicly available and can be extended to explore other aggregation methods and other data distributions.