Description Usage Arguments Details Value Examples
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.
1 2 | 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | library(ssc)
## 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) # scale the attributes
## Prepare data
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,] # training instances
ytrain <- y[tra.idx] # classes of training instances
# 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 # remove class information of unlabeled instances
# 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 testing instances
## 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, ])
md1 <- selfTrainingG(y = ytrain, gen.learner, gen.pred)
cls1 <- predict(md1$model, xitest, type = "class")
table(cls1, yitest)
## Example: Training from a distance matrix with 1-NN (oneNN) as base classifier.
dtrain <- as.matrix(proxy::dist(x = xtrain, method = "euclidean", by_rows = TRUE))
gen.learner <- function(indexes, cls) {
m <- ssc::oneNN(y = cls)
attr(m, "tra.idxs") <- indexes
m
}
gen.pred <- function(model, indexes) {
tra.idxs <- attr(model, "tra.idxs")
d <- dtrain[indexes, tra.idxs]
prob <- predict(model, d, distance.weighting = "none")
prob
}
md2 <- selfTrainingG(y = ytrain, gen.learner, gen.pred)
ditest <- proxy::dist(x = xitest, y = xtrain[md2$instances.index,],
method = "euclidean", by_rows = TRUE)
cls2 <- predict(md2$model, ditest, type = "class")
table(cls2, yitest)
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