Learning to Aggregate Using Uninorms
In this paper, we propose a framework for a class of learning problems that we refer to as “learning to aggregate”. Roughly, learning-to-aggregate problems are supervised machine learning problems, in which instances are represented in the form of a composition of a (variable) number on constituents; such compositions are associated with an evaluation, score, or label, 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. Our learning-to-aggregate framework establishes a close connection between machine learning and a branch of mathematics devoted to the systematic study of aggregation functions. We specifically focus on a class of functions called uninorms, which combine conjunctive and disjunctive modes of aggregation. Experimental results for a corresponding model are presented for a review data set, for which the aggregation problem consists of combining different reviewer opinions about a paper into an overall decision of acceptance or rejection.
KeywordsFeature Vector Learning Problem Aggregation Function Neutral Element Preference Learning
We thank Pritha Gupta and Karlson Pfannschmidt for their helpful suggestions. This work is part of the Collaborative Research Center “On-the-Fly Computing”, which is supported by the German Research Foundation (DFG).
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