New R package SSLfmm tackles semi-supervised learning with missing labels

A new R package named SSLfmm has been developed for semi-supervised learning, specifically addressing scenarios with mixed missingness in class labels. The package implements a likelihood-based Gaussian finite-mixture classification approach that models the label-missingness process alongside the class distribution. SSLfmm supports various missingness mechanisms, including complete-case, missing completely at random (MCAR), and missing at random (MAR), and offers a unified R interface for fitting, prediction, and diagnostics. AI

IMPACT Provides a new tool for researchers and practitioners dealing with incomplete labeled data in machine learning tasks.

RANK_REASON The cluster is about a new R package for semi-supervised learning published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• Jinran Wu
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