SSLR (version 0.9.3.3)

snnrce: General Interface for SNNRCE model

Description

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.

Usage

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

Arguments

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.

Value

(When model fit) 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.

Details

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.

References

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.

Examples

# NOT RUN {
library(tidyverse)
library(tidymodels)
library(caret)
library(SSLR)

data(wine)
set.seed(1)
train.index <- createDataPartition(wine$Wine, p = .7, list = FALSE)
train <- wine[ train.index,]
test  <- wine[-train.index,]

cls <- which(colnames(wine) == "Wine")

#% LABELED
labeled.index <- createDataPartition(wine$Wine, p = .2, list = FALSE)
train[-labeled.index,cls] <- NA


m <- snnrce(x.inst = TRUE,
            dist = "Euclidean",
            alpha = 0.1) %>% fit(Wine ~ ., data = train)



predict(m,test) %>%
  bind_cols(test) %>%
  metrics(truth = "Wine", estimate = .pred_class)
# }