Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Set-valued classification -- overview via a unified framework   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.

18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
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The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an