CoBC generic method
CoBC is a semi-supervised learning algorithm with a co-training
style. This algorithm trains N classifiers with the learning scheme defined in
gen.learner using a reduced set of labeled examples. For each iteration, an unlabeled
example is labeled for a classifier if the most confident classifications assigned by the
other N-1 classifiers agree on the labeling proposed. The unlabeled examples
candidates are selected randomly from a pool of size u.
coBCG(y, gen.learner, gen.pred, N = 3, perc.full = 0.7, u = 100, max.iter = 50)
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 |
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 |
N |
The number of classifiers used as committee members. All these classifiers
are trained using the |
perc.full |
A number between 0 and 1. If the percentage of new labeled examples reaches this value the self-labeling process is stopped. Default is 0.7. |
u |
Number of unlabeled instances in the pool. Default is 100. |
max.iter |
Maximum number of iterations to execute in the self-labeling process. Default is 50. |
coBCG can be helpful in those cases where the method selected as
base classifier needs a learner and pred functions with other
specifications. For more information about the general coBC method,
please see coBC function. Essentially, coBC
function is a wrapper of coBCG function.
A list object of class "coBCG" containing:
The final N base classifiers trained using the enlarged labeled set.
List of N vectors of indexes related to the training instances
used per each classifier. These indexes are relative to the y argument.
The indexes of all training instances used to
train the N models. These indexes include the initial labeled instances
and the newly labeled instances. These indexes are relative to the y argument.
List of three vectors with the same information in model.index
but the indexes are relative to instances.index vector.
The levels of y factor.
library(SSLR)
library(caret)
## 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.learner1 <- function(indexes, cls)
caret::knn3(x = xtrain[indexes,], y = cls, k = 1)
gen.pred1 <- function(model, indexes)
predict(model, xtrain[indexes,])
set.seed(1)
trControl_coBCG <- list(gen.learner = gen.learner1, gen.pred = gen.pred1)
md1 <- train_generic(ytrain, method = "coBCG", trControl = trControl_coBCG)
# Predict probabilities per instances using each model
h.prob <- lapply(
X = md1$model,
FUN = function(m) predict(m, xitest)
)
# Combine the predictions
cls1 <- coBCCombine(h.prob, md1$classes)
table(cls1, yitest)
confusionMatrix(cls1, yitest)$overall[1]
## Example: Training from a distance matrix with 1-NN (oneNN) as base classifier.
dtrain <- as.matrix(proxy::dist(x = xtrain, method = "euclidean", by_rows = TRUE))
gen.learner2 <- function(indexes, cls) {
m <- SSLR::oneNN(y = cls)
attr(m, "tra.idxs") <- indexes
m
}
gen.pred2 <- function(model, indexes) {
tra.idxs <- attr(model, "tra.idxs")
d <- dtrain[indexes, tra.idxs]
prob <- predict(model, d, distance.weighting = "none")
prob
}
set.seed(1)
trControl_coBCG2 <- list(gen.learner = gen.learner2, gen.pred = gen.pred2)
md2 <- train_generic(ytrain, method = "coBCG", trControl = trControl_coBCG2)
# Predict probabilities per instances using each model
ditest <- proxy::dist(x = xitest, y = xtrain[md2$instances.index,],
method = "euclidean", by_rows = TRUE)
h.prob <- list()
ninstances <- nrow(dtrain)
for (i in 1:length(md2$model)) {
m <- md2$model[[i]]
D <- ditest[, md2$model.index.map[[i]]]
h.prob[[i]] <- predict(m, D)
}
# Combine the predictions
cls2 <- coBCCombine(h.prob, md2$classes)
table(cls2, yitest)
confusionMatrix(cls2, yitest)$overall[1]Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.