Self-training generic method
Self-training is a simple and effective semi-supervised learning classification method. The self-training classifier is initially trained with a reduced set of labeled examples. Then it is iteratively retrained with its own most confident predictions over the unlabeled examples. Self-training follows a wrapper methodology using one base supervised classifier to establish the possible class of unlabeled instances.
selfTrainingG( y, gen.learner, gen.pred, max.iter = 50, perc.full = 0.7, thr.conf = 0.5 )
y |
A vector with the labels of training instances. In this vector the
unlabeled instances are specified with the value |
gen.learner |
A function for training a supervised base classifier. This function needs two parameters, indexes and cls, where indexes indicates the instances to use and cls specifies the classes of those instances. |
gen.pred |
A function for predicting the probabilities per classes.
This function must be two parameters, model and indexes, where the model
is a classifier trained with |
max.iter |
Maximum number of iterations to execute the self-labeling process. Default is 50. |
perc.full |
A number between 0 and 1. If the percentage of new labeled examples reaches this value the self-training process is stopped. Default is 0.7. |
thr.conf |
A number between 0 and 1 that indicates the confidence theshold.
At each iteration, only the newly labelled examples with a confidence greater than
this value ( |
SelfTrainingG can be helpful in those cases where the method selected as
base classifier needs learner and pred functions with other
specifications. For more information about the general self-training method,
please see the selfTraining function. Essentially, the selfTraining
function is a wrapper of the selfTrainingG function.
A list object of class "selfTrainingG" containing:
The final base classifier trained using the enlarged labeled set.
The indexes of the training instances used to
train the model. These indexes include the initial labeled instances
and the newly labeled instances.
Those indexes are relative to the y argument.
library(SSLR)
## Load Wine data set
data(wine)
cls <- which(colnames(wine) == "Wine")
x <- wine[, - cls] # instances without classes
y <- wine[, cls] # the classes
x <- scale(x)
set.seed(20)
# Use 50% of instances for training
tra.idx <- sample(x = length(y), size = ceiling(length(y) * 0.5))
xtrain <- x[tra.idx,]
ytrain <- y[tra.idx]
# Use 70% of train instances as unlabeled set
tra.na.idx <- sample(x = length(tra.idx), size = ceiling(length(tra.idx) * 0.7))
ytrain[tra.na.idx] <- NA
# Use the other 50% of instances for inductive testing
tst.idx <- setdiff(1:length(y), tra.idx)
xitest <- x[tst.idx,] # testing instances
yitest <- y[tst.idx] # classes of instances in xitest
# Use the unlabeled examples for transductive testing
xttest <- x[tra.idx[tra.na.idx],] # transductive testing instances
yttest <- y[tra.idx[tra.na.idx]] # classes of instances in xttest
library(caret)
#PREPARE DATA
data <- cbind(xtrain, Class = ytrain)
dtrain <- as.matrix(proxy::dist(x = xtrain, method = "euclidean", by_rows = TRUE))
ditest <- as.matrix(proxy::dist(x = xitest, y = xtrain, method = "euclidean", by_rows = TRUE))
ddata <- cbind(dtrain, Class = ytrain)
ddata <- as.data.frame(ddata)
ktrain <- as.matrix(exp(-0.048 * dtrain ^ 2))
kdata <- cbind(ktrain, Class = ytrain)
kdata <- as.data.frame(kdata)
ktrain <- as.matrix(exp(-0.048 * dtrain ^ 2))
kitest <- as.matrix(exp(-0.048 * ditest ^ 2))
## Example: Training from a set of instances with 1-NN (knn3) as base classifier.
gen.learner <- function(indexes, cls)
caret::knn3(x = xtrain[indexes,], y = cls, k = 1)
gen.pred <- function(model, indexes)
predict(model, xtrain[indexes,])
trControl_selfTrainingG1 <- list(gen.learner = gen.learner, gen.pred = gen.pred)
md1 <- train_generic(ytrain, method = "selfTrainingG", trControl = trControl_selfTrainingG1)
p1 <- predict(md1$model, xitest, type = "class")
table(p1, yitest)
confusionMatrix(p1, yitest)$overall[1]
## Example: Training from a distance matrix with 1-NN (oneNN) as base classifier.
dtrain <- as.matrix(proxy::dist(x = xtrain, method = "euclidean", by_rows = TRUE))
gen.learner <- function(indexes, cls) {
m <- SSLR::oneNN(y = cls)
attr(m, "tra.idxs") <- indexes
m
}
gen.pred <- function(model, indexes) {
tra.idxs <- attr(model, "tra.idxs")
d <- dtrain[indexes, tra.idxs]
prob <- predict(model, d, distance.weighting = "none")
prob
}
trControl_selfTrainingG2 <- list(gen.learner = gen.learner, gen.pred = gen.pred)
md2 <- train_generic(ytrain, method = "selfTrainingG", trControl = trControl_selfTrainingG2)
ditest <- proxy::dist(x = xitest, y = xtrain[md2$instances.index,],
method = "euclidean", by_rows = TRUE)
p2 <- predict(md2$model, ditest, type = "class")
table(p2, yitest)
confusionMatrix(p2, yitest)$overall[1]Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.