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Informative Label Missingness in Multiclass Classification Information Geometry and Excess Risk

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Informative label missingness can change the usual efficiency ordering between completely and partially labelled classifiers because the pattern of missing labels may itself carry information about the classification model. We develop a general likelihood-based theory for this phenomenon in parametric multiclass classification. An efficient-information decomposition separates information lost through unavailable class memberships from information contributed by the missing-label mechanism. We then derive a quadratic expansion of plug-in excess risk over the active pairwise faces of the multicl

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