coBCG {ssc}

CoBC generic method

Description

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.

Usage

coBCG(y, gen.learner, gen.pred, N = 3, perc.full = 0.7, u = 100,
  max.iter = 50)

Arguments

y

A vector with the labels of training instances. In this vector the unlabeled instances are specified with the value NA.

gen.learner

A function for training N supervised base classifiers. 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 gen.learner function and indexes indicates the instances to predict.

N

The number of classifiers used as committee members. All these classifiers are trained using the gen.learner function. Default is 3.

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.

Details

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.

Value

A list object of class "coBCG" containing:

model

The final N base classifiers trained using the enlarged labeled set.

model.index

List of N vectors of indexes related to the training instances used per each classifier. These indexes are relative to the y argument.

instances.index

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.

model.index.map

List of three vectors with the same information in model.index but the indexes are relative to instances.index vector.

classes

The levels of y factor.

Examples

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.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)
md1 <- coBCG(y = ytrain, gen.learner1, gen.pred1)

# 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)

## 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 <- ssc::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)
md2 <- coBCG(y = ytrain, gen.learner2, gen.pred2)

# 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)