Decision-Target Reversal under Informative Label Missingness
Informative label missingness can improve classification efficiency because the pattern of label availability may itself contain information about the discriminant structure. We show that this advantage need not persist across deployment objectives: with the data-generating model and statistical experiments held fixed, informative partial classification can be favourable under one loss and unfavourable under another. We call this phenomenon decision-target reversal. For binary Gaussian discrimination, we derive a local cost-sensitive excess-risk expansion and, in one dimension, obtain explicit critical cost ratios at which the first-order risk ordering reverses. We show that the required ...
| Authors | Farinborz Setoudehtazangi, Geoffrey J. McLachlan |
|---|---|
| Primary category | stat.ML |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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