SelfLearning: Self-Learning approach to Semi-supervised Learning

In jkrijthe/RSSL: Implementations of Semi-Supervised Learning Approaches for Classification

Self-Learning approach to Semi-supervised Learning

Description

Use self-learning (also known as Yarowsky's algorithm or pseudo-labeling) to turn any supervised classifier into a semi-supervised method by iteratively labeling the unlabeled objects and adding these predictions to the set of labeled objects until the classifier converges.

Usage

SelfLearning(X, y, X_u = NULL, method, prob = FALSE, cautious = FALSE,
  max_iter = 100, ...)

Arguments

X	matrix; Design matrix for labeled data
y	factor or integer vector; Label vector
X_u	matrix; Design matrix for unlabeled data
method	Supervised classifier to use. Any function that accepts as its first argument a design matrix X and as its second argument a vector of labels y and that has a predict method.
prob	Not used
cautious	Not used
max_iter	integer; Maximum number of iterations
...	additional arguments to be passed to method

References

McLachlan, G.J., 1975. Iterative Reclassification Procedure for Constructing an Asymptotically Optimal Rule of Allocation in Discriminant Analysis. Journal of the American Statistical Association, 70(350), pp.365-369.

Yarowsky, D., 1995. Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of the 33rd annual meeting on Association for Computational Linguistics, pp.189-196.

See Also

Other RSSL classifiers: EMLeastSquaresClassifier, EMLinearDiscriminantClassifier, GRFClassifier, ICLeastSquaresClassifier, ICLinearDiscriminantClassifier, KernelLeastSquaresClassifier, LaplacianKernelLeastSquaresClassifier(), LaplacianSVM, LeastSquaresClassifier, LinearDiscriminantClassifier, LinearSVM, LinearTSVM(), LogisticLossClassifier, LogisticRegression, MCLinearDiscriminantClassifier, MCNearestMeanClassifier, MCPLDA, MajorityClassClassifier, NearestMeanClassifier, QuadraticDiscriminantClassifier, S4VM, SVM, TSVM, USMLeastSquaresClassifier, WellSVM, svmlin()

Examples

data(testdata)
t_self <- SelfLearning(testdata$X,testdata$y,testdata$X_u,method=NearestMeanClassifier)
t_sup <- NearestMeanClassifier(testdata$X,testdata$y)
# Classification Error
1-mean(predict(t_self, testdata$X_test)==testdata$y_test) 
1-mean(predict(t_sup, testdata$X_test)==testdata$y_test) 
loss(t_self, testdata$X_test, testdata$y_test)

jkrijthe/RSSL documentation built on Jan. 13, 2024, 1:56 a.m.