Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Consistent Classification with Generalized Metrics   We propose a framework for constructing and analyzing multiclass and
multioutput classification metrics, i.e., involving multiple, possibly
correlated multiclass labels. Our analysis reveals novel insights on the
geometry of feasible confusion tensors -- including necessary and sufficient
conditions for the equivalence between optimizing an arbitrary non-decomposable
metric and learning a weighted classifier. Further, we analyze averaging
methodologies commonly used to compute multioutput metrics and characterize the
corresponding Bayes optimal classifiers. We show that the plug-in estimator
based on this characterization is consistent and is easily implemented as a
post-processing rule. Empirical results on synthetic and benchmark datasets
support the theoretical findings.

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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: atom-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat