Accepted Paper: Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA

Back to list of accepted papers


Vitalik Melnikov (Paderborn University); Eyke Hüllermeier (University of Paderborn)


The problem of "learning to aggregate" (LTA) has recently been introduced as a novel machine learning setting, in which instances are represented in the form of a composition of a (variable) number on constituents. Such compositions are associated with an evaluation, which is the target of the prediction task, and which can presumably be modeled in the form of a suitable aggregation of the properties of its constituents. An especially interesting class of LTA problems arises when the evaluations of the constituents are not available at training time, and instead ought to be learned simultaneously with the aggregation function. This scenario is referred to as the "aggregation/disaggregation problem". In this paper, we tackle this problem for a specifically interesting type of aggregation function, namely the Ordered Weighted Averaging (OWA) operator. In particular, we provide an efficient algorithm for learning the OWA parameters together with local utility scores of the constituents, and evaluate this algorithm in a case study on predicting the performance of classifier ensembles.