A collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM.
This R package provides implementations of several semi-supervised learning methods, in particular, our own work involving constraint based semi-supervised learning.
The package is still under development. Therefore, function names and interfaces are subject to change.
To cite the package, use either of these two references:
This package is not yet available on CRAN. 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)df <- 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 national program COMMIT.