Semi-Supervised Learning (SSL) is implemented when algorithms are trained on both labeled and unlabeled data. This is a very common application of ML as it is unrealistic to obtain a fully labeled dataset. Researchers have tackled three main issues: missing at random (MAR), missing completely at random (MCAR), and missing not at random (MNAR). The MNAR problem is the most challenging of the three as one cannot safely assume that all class distributions are equal. Existing methods, including Class-Aware Imputation (CAI) and Class-Aware Propensity (CAP), mostly overlook the non-randomness in the unlabeled data. This paper proposes two new methods of combining multiple imputation models to achieve higher accuracy and less bias. 1) We use multiple imputation models, create confidence intervals, and apply a threshold to ignore pseudo-labels with low confidence. 2) Our new method, SSL with De-biased Imputations (SSL-DI), aims to reduce bias by filtering out inaccurate data and finding a subset that is accurate and reliable. This subset of the larger dataset could be imputed into another SSL model, which will be less biased. The proposed models have been shown to be effective in both MCAR and MNAR situations, and experimental results show that our methodology outperforms existing methods in terms of classification accuracy and reducing bias.

翻译：半监督学习（SSL）是在算法同时使用有标签和无标签数据进行训练时实现的。这是机器学习中非常常见的应用，因为获得完全标记的数据集是不现实的。研究人员已解决三个主要问题：随机缺失（MAR）、完全随机缺失（MCAR）和非随机缺失（MNAR）。其中MNAR问题最具挑战性，因为无法安全假设所有类别分布相等。现有方法（包括类别感知插补CAI和类别感知倾向CAP）大多忽视无标签数据中的非随机性。本文提出两种结合多重插补模型以实现更高精度和更少偏差的新方法：1）使用多重插补模型创建置信区间，并应用阈值忽略低置信度的伪标签；2）我们提出的新方法——基于去偏插补的半监督学习（SSL-DI），旨在通过过滤不准确数据并找到准确可靠的子集来减少偏差。该较大数据集的子集可输入另一个SSL模型，从而降低偏差。实验证明，所提模型在MCAR和MNAR情况下均有效，且实验结果显示，我们的方法在分类准确率和降低偏差方面优于现有方法。