ssc: An R Package for Semi-Supervised Classification

Semi-supervised classification has become a popular area of machine learning, where both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. We present the R package ssc (cran.r-project...) that implements a collection of self-labeled techniques to construct a classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented in the ssc package can be applied to classification problems in several domains by the specification of a suitable learning scheme. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.

[@duvvurum]

Thank you very much for Building the package and sharing the features via this video.

[@user-vy6fn7uc5k]

Thank you for the great presentation. How do you test the performance? Considering most data points are unlabeled?

[@nguyenhaituan9838]

Thank you very much for sharing this video

[@murraystaff568]

Great presentation 👍👍