Induced ordered weighted averaging operators

TLDR

OWA operators aggregate pairs of values by inducing an ordering on one component and then aggregating the other component according to that ordering. The paper aims to introduce the Induced Ordered Weighted Averaging (IOWA) operator and to present a method for learning its weighting vector from observational data. The authors describe how to learn the weighting vector for OWA/IOWA operators from data and demonstrate that the resulting tool can represent various aggregation models. They show that many different aggregation situations can be represented within the IOWA framework.

Abstract

We briefly describe the Ordered Weighted Averaging (OWA) operator and discuss a methodology for learning the associated weighting vector from observational data. We then introduce a more general type of OWA operator called the Induced Ordered Weighted Averaging (IOWA) Operator. These operators take as their argument pairs, called OWA pairs, in which one component is used to induce an ordering over the second components which are then aggregated. A number of different aggregation situations have been shown to be representable in this framework. We then show how this tool can be used to represent different types of aggregation models.