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This R package provides implementations of several semi-supervisedlearning methods, in particular, our own work involving constraint basedsemi-supervised learning.

To cite the package, use either of these two references:

• Krijthe, J. H. (2016). RSSL: R package for Semi-supervised Learning.In B. Kerautret, M. Colom, & P. Monasse (Eds.), Reproducible Researchin Pattern Recognition. RRPR 2016. Lecture Notes in Computer Science,vol 10214. (pp. 104–115). Springer International Publishing.https://doi.org/10.1007/978-3-319-56414-2_8. arxiv:https://arxiv.org/abs/1612.07993
• Krijthe, J.H. & Loog, M. (2015). Implicitly ConstrainedSemi-Supervised Least Squares Classification. In E. Fromont, T. deBie, & M. van Leeuwen, eds. 14th International Symposium on Advancesin Intelligent Data Analysis XIV (Lecture Notes in Computer ScienceVolume 9385). Saint Etienne. France, pp. 158-169.

This package available on CRAN. The easiest way to install the packageis to use:

install.packages("RSSL")

To install the latest version of the package using the devtools package:

library(devtools)install_github("jkrijthe/RSSL")

After installation, load the package as usual:

library(RSSL)

The following code generates a simple dataset, trains a supervised andtwo semi-supervised classifiers and evaluates their performance:

library(dplyr,warn.conflicts=FALSE)library(ggplot2,warn.conflicts=FALSE)set.seed(2)df<- generate2ClassGaussian(200,d=2,var=0.2,expected=TRUE)# Randomly remove labelsdf<-df %>% add_missinglabels_mar(Class~.,prob=0.98)# Train classifierg_nm<- NearestMeanClassifier(Class~.,df,prior=matrix(0.5,2))g_self<- SelfLearning(Class~.,df,method=NearestMeanClassifier,prior=matrix(0.5,2))# Plot datasetdf %>%   ggplot(aes(x=X1,y=X2,color=Class,size=Class))+  geom_point()+  coord_equal()+  scale_size_manual(values=c("-1"=3,"1"=3),na.value=1)+  geom_linearclassifier("Supervised"=g_nm,"Semi-supervised"=g_self)

# Evaluate performance: Squared Loss & Error Ratemean(loss(g_nm,df))mean(loss(g_self,df))mean(predict(g_nm,df)!=df$Class)mean(predict(g_self,df)!=df$Class)

Work on this package was supported by Project 23 of the Dutch nationalprogram COMMIT.