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Linear Classifiers Under Infinite Imbalance. (arXiv:2106.05797v2 [stat.ML] UPDATED)
stat.ML updates on arXiv.org arxiv.org
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 empirical 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 almost
sure limit under infinite imbalance, extending prior work …
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