Semi-Supervised Learning with Multiple Imputations on Non-Random Missing
  Labels
Jason Lu Michael Ma Huaze Xu Zixi Xu
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.