9 citations · 9 across the 8 of their papers we have counts for
9 papers
Large Classification-Risk-Optional Label Acquisition
F. Setoudehtanzangi, Geoffrey J. McLachlan
We study how a limited labeling budget should be allocated to minimize multiclass zero-one classification risk. We consider parametric classification problems in which features are…
Semi-Supervised Classification with Informative Missing Labels in Weibull Mixture Models
Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan
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…
Deep Skew-t Mixture Models
Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan
High-dimensional clustering is challenging when component distributions are both heavy-tailed and directionally asymmetric. We propose a deep skew- mixture model (DStMM), a hier…
Informative Label Missingness in Multiclass Classification Information Geometry and Excess Risk
Fariborz Setoudehtazang, Geoffrey J. McLachlan
Informative label missingness can change the usual efficiency ordering between completely and partially labelled classifiers because the pattern of missing labels may itself carry…
Learning from Uncertainty-dependent Missing Labels for Semi-supervised Classification
You-Gan Wang, Jinran Wu, Geoffrey J. McLachlan
Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervised setting in which the probability of label missingness depends on t…
Favourable Missingness in Semi-Supervised Classification for Exponential Mixture Models
Huanchao Zhou, Jinran Wu, Fariborz Setoudehtazang +1
Semi-supervised classifiers are commonly trained from samples in which all features are observed but some class labels are missing. When label missingness is independent of the obs…