Semi-Supervised Classification with Informative Missing Labels in Weibull Mixture Models
We consider semi-supervised classification from a partially classified sample arising from a two-component Weibull mixture. The feature is observed for all data, whereas some class labels are missing. The probability of a missing label is modelled as a function of classification uncertainty, giving a feature-dependent missing-at-random (MAR) mechanism that shares parameters with the Weibull-mixture classifier. The missing-label indicators can therefore provide information about the classifier in addition to the observed features and available class labels. Under a common Weibull shape, a Bayes' rule has at most one positive decision boundary, which is unique when the rule is nonconstant; ...
| Authors | Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan |
|---|---|
| Primary category | stat.ML |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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