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Rodemann, Julian ORCID logoORCID: https://orcid.org/0000-0001-6112-4136; Kreiss, Dominik; Hüllermeier, Eyke ORCID logoORCID: https://orcid.org/0000-0002-9944-4108 and Augustin, Thomas (October 2022): Levelwise Data Disambiguation by Cautious Superset Classification. 15th International Conference on Scalable Uncertainty Management, Paris, France, 17-19 October 2022. In: Proceedings of the 15th International Conference on Scalable Uncertainty Management, Vol. 13562 Cham: Springer. pp. 263-276

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Drawing conclusions from set-valued data calls for a trade-off between caution and precision. In this paper, we propose a way to construct a hierarchical family of subsets within set-valued categorical observations. Each subset corresponds to a level of cautiousness, the smallest one as a singleton representing the most optimistic choice. To achieve this, we extend the framework of Optimistic Superset Learning (OSL), which disambiguates set-valued data by determining the singleton corresponding to the most predictive model. We utilize a variant of OSL for classification with 0/1 loss to find the instantiations whose corresponding empirical risks are below context-depending thresholds. Varying this threshold induces a hierarchy among those instantiations. In order to rule out ties corresponding to the same classification error, we utilize a hyperparameter of Support Vector Machines (SVM) that controls the model’s complexity. We twist the tuning of this hyperparameter to find instantiations whose optimal separations have the greatest generality. Finally, we apply our method on the prototypical example of yet undecided political voters as set-valued observations. To this end, we use both simulated data and pre-election polls by Civey including undecided voters for the 2021 German federal election.

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