Set-valued classification -- overview via a unified framework

• URL: http://arxiv.org/abs/2102.12318v1
• Date: Wed, 24 Feb 2021 14:54:07 GMT
• Title: Set-valued classification -- overview via a unified framework
• Authors: Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Titouan Lorieul
• Abstract summary: Multi-class datasets can be extremely ambiguous and single-output predictions fail to deliver satisfactory performance. By allowing predictors to predict a set of label candidates, set-valued classification offers a natural way to deal with this ambiguity. We provide infinite sample optimal set-valued classification strategies and review a general plug-in principle to construct data-driven algorithms.
• Score: 15.109906768606644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-class classification problem is among the most popular and well-studied statistical frameworks. Modern multi-class datasets can be extremely ambiguous and single-output predictions fail to deliver satisfactory performance. By allowing predictors to predict a set of label candidates, set-valued classification offers a natural way to deal with this ambiguity. Several formulations of set-valued classification are available in the literature and each of them leads to different prediction strategies. The present survey aims to review popular formulations using a unified statistical framework. The proposed framework encompasses previously considered and leads to new formulations as well as it allows to understand underlying trade-offs of each formulation. We provide infinite sample optimal set-valued classification strategies and review a general plug-in principle to construct data-driven algorithms. The exposition is supported by examples and pointers to both theoretical and practical contributions. Finally, we provide experiments on real-world datasets comparing these approaches in practice and providing general practical guidelines.

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