Semi-supervised learning (SSL) approaches have been successfully applied in a wide range of engineering and scientific fields. This paper investigates the generative model framework with a missingness mechanism for unclassified observations, as introduced by Ahfock and McLachlan(2020). We show that in a partially classified sample, a classifier using Bayes rule of allocation with a missing-data mechanism can surpass a fully supervised classifier in a two-class normal homoscedastic model, especially with moderate to low overlap and proportion of missing class labels, or with large overlap but few missing labels. It also outperforms a classifier with no missing-data mechanism regardless of the overlap region or the proportion of missing class labels. Our exploration of two- and three-component normal mixture models with unequal covariances through simulations further corroborates our findings. Finally, we illustrate the use of the proposed classifier with a missing-data mechanism on interneuronal and skin lesion datasets.

翻译：半监督学习(SSL)方法已成功应用于广泛的工程和科学领域。本文研究了Ahfock和McLachlan(2020)提出的具有缺失机制的非分类观测生成模型框架。我们证明，在部分分类样本中，采用带缺失数据机制的贝叶斯分配规则分类器能够超越完全监督分类器的性能，尤其是在两类正态同方差模型中，当类别重叠程度中等至较低且缺失类别标签比例适中时，或当重叠程度较大但缺失标签较少时。该分类器还优于不包含缺失数据机制的分类器，无论重叠区域或缺失类别标签比例如何。通过模拟对具有不等协方差的两分量和三分量正态混合模型的探索进一步证实了我们的发现。最后，我们展示了所提出的带缺失数据机制分类器在中间神经元和皮肤病变数据集上的应用。