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This R package provides implementations of several semi-supervised learning methods, in particular, our own work involving constraint based semi-supervised learning.
To cite the package, use either of these two references:
This package available on CRAN. The easiest way to install the package is to use:
To install the latest version of the package using the devtools package:
After installation, load the package as usual:
The following code generates a simple dataset, trains a supervised and two 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 labels
df <- df %>% add_missinglabels_mar(Class~.,prob=0.98)
# Train classifier
g_nm <- NearestMeanClassifier(Class~.,df,prior=matrix(0.5,2))
g_self <- SelfLearning(Class~.,df,
method=NearestMeanClassifier,
prior=matrix(0.5,2))
# Plot dataset
df %>%
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 Rate
mean(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 national program COMMIT.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.