Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Linear Classifiers Under Infinite Imbalance   We study the behavior of linear discriminant functions for binary
classification in the infinite-imbalance limit, where the sample size of one
class grows without bound while the sample size of the other remains fixed. The
coefficients of the classifier minimize an expected loss specified through a
weight function. We show that for a broad class of weight functions, the
intercept diverges but the rest of the coefficient vector has a finite limit
under infinite imbalance, extending prior work on logistic regression. The
limit depends on the left tail of the weight function, for which we distinguish
three cases: bounded, asymptotically polynomial, and asymptotically
exponential. The limiting coefficient vectors reflect robustness or
conservatism properties in the sense that they optimize against certain
worst-case alternatives. In the bounded and polynomial cases, the limit is
equivalent to an implicit choice of upsampling distribution for the minority
class. We apply these ideas in a credit risk setting, with particular emphasis
on performance in the high-sensitivity and high-specificity regions.

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The categories of this paper are: atom-ph
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The categories of this paper are: math
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The categories of this paper are: physics
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol