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.