snnrce: SNNRCE method
In ssc: Semi-Supervised Classification Methods

Description Usage Arguments Details Value References Examples

SNNRCE (Self-training Nearest Neighbor Rule using Cut Edges) is a variant of the self-training classification method (selfTraining) with a different addition mechanism and a fixed learning scheme (1-NN). SNNRCE uses an amending scheme to avoid the introduction of noisy examples into the enlarged labeled set. The mislabeled examples are identified using the local information provided by the neighborhood graph. A statistical test using cut edge weight is used to modify the labels of the missclassified examples.

1	snnrce(x, y, x.inst = TRUE, dist = "Euclidean", alpha = 0.1)

`x`	A object that can be coerced as matrix. This object has two possible interpretations according to the value set in the `x.inst` argument: a matrix with the training instances where each row represents a single instance or a precomputed distance matrix between the training examples.
`y`	A vector with the labels of the training instances. In this vector the unlabeled instances are specified with the value `NA`.
`x.inst`	A boolean value that indicates if `x` is or not an instance matrix. Default is `TRUE`.
`dist`	A distance function available in the `proxy` package to compute the distance matrix in the case that `x.inst` is `TRUE`.
`alpha`	Rejection threshold to test the critical region. Default is 0.1.

SNNRCE initiates the self-labeling process by training a 1-NN from the original labeled set. This method attempts to reduce the noise in examples by labeling those instances with no cut edges in the initial stages of self-labeling learning. These highly confident examples are added into the training set. The remaining examples follow the standard self-training process until a minimum number of examples will be labeled for each class. A statistical test using cut edge weight is used to modify the labels of the missclassified examples The value of the alpha argument defines the critical region where the candidates examples are tested. The higher this value is, the more relaxed it is the selection of the examples that are considered mislabeled.

A list object of class "snnrce" containing:

model: The final base classifier trained using the enlarged labeled set.
instances.index: 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 x argument.
classes: The levels of y factor.
x.inst: The value provided in the x.inst argument.
dist: The value provided in the dist argument when x.inst is TRUE.
xtrain: A matrix with the subset of training instances referenced by the indexes instances.index when x.inst is TRUE.

Yu Wang, Xiaoyan Xu, Haifeng Zhao, and Zhongsheng Hua.
Semisupervised learning based on nearest neighbor rule and cut edges.
Knowledge-Based Systems, 23(6):547-554, 2010. ISSN 0950-7051. doi: http://dx.doi.org/10.1016/j.knosys.2010.03.012.

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 as base classifier.
m1 <- snnrce(x = xtrain, y = ytrain,  dist = "Euclidean")
pred1 <- predict(m1, xitest)
table(pred1, yitest)

## Example: Training from a distance matrix with 1-NN as base classifier.
dtrain <- proxy::dist(x = xtrain, method = "euclidean", by_rows = TRUE)
m2 <- snnrce(x = dtrain, y = ytrain, x.inst = FALSE)
ditest <- proxy::dist(x = xitest, y = xtrain[m2$instances.index,],
                      method = "euclidean", by_rows = TRUE)
pred2 <- predict(m2, ditest)
table(pred2, yitest)