SSLfmm: An R Package for Semi-Supervised Learning with Mixed Missingness
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Abstract (arXiv)
Partially labelled samples arise when features are observed for data, but class labels are available for only a subset. In such settings, the mechanism governing label availability may itself contain information relevant to classification, yet it is typically left unmodelled in standard semi-supervised learning procedures. The SSLfmm package implements likelihood-based Gaussian finite-mixture classification in which the label-missingness process is modelled jointly with the class distribution. It supports complete-case, missing completely at random (MCAR), entropy-based missing at random (MAR), and mixed analyses in which MCAR and MAR mechanisms may both contribute. For the mixed mechanism, the source of a missing label may be known or unknown. A common R interface is provided for model fitting, prediction, performance assessment, and simulation. We describe the statistical formulation and software implementation and demonstrate its use through reproducible simulation and a semi-synthetic Blood Transfusion application.
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References · 4
- 2019An Apparent Paradox: A Classifier Trained from a Partially Classified Sample May Have Smaller Expected Error Rate Than That If the Sample Were Completely Classifiedcited by 8
- 2021Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Reviewcited by 7
- 2023Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanismcited by 5
- 2015EMMIXcskew: an R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributionscited by 1
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