General Interface for SNNRCE model
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.
snnrce(x.inst = TRUE, dist = "Euclidean", alpha = 0.1)
x.inst |
A boolean value that indicates if |
dist |
A distance function available in the |
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.
(When model fit) A list object of class "snnrce" 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 x argument.
The levels of y factor.
The value provided in the x.inst argument.
The value provided in the dist argument when x.inst is TRUE.
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(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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.