Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convex Calibrated Surrogates for the Multi-Label F-Measure   The F-measure is a widely used performance measure for multi-label
classification, where multiple labels can be active in an instance
simultaneously (e.g. in image tagging, multiple tags can be active in any
image). In particular, the F-measure explicitly balances recall (fraction of
active labels predicted to be active) and precision (fraction of labels
predicted to be active that are actually so), both of which are important in
evaluating the overall performance of a multi-label classifier. As with most
discrete prediction problems, however, directly optimizing the F-measure is
computationally hard. In this paper, we explore the question of designing
convex surrogate losses that are calibrated for the F-measure -- specifically,
that have the property that minimizing the surrogate loss yields (in the limit
of sufficient data) a Bayes optimal multi-label classifier for the F-measure.
We show that the F-measure for an $s$-label problem, when viewed as a $2^s
\times 2^s$ loss matrix, has rank at most $s^2+1$, and apply a result of
Ramaswamy et al. (2014) to design a family of convex calibrated surrogates for
the F-measure. The resulting surrogate risk minimization algorithms can be
viewed as decomposing the multi-label F-measure learning problem into $s^2+1$
binary class probability estimation problems. We also provide a quantitative
regret transfer bound for our surrogates, which allows any regret guarantees
for the binary problems to be transferred to regret guarantees for the overall
F-measure problem, and discuss a connection with the algorithm of Dembczynski
et al. (2013). Our experiments confirm our theoretical findings.

17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys