SSLR (version 0.9.3.3)

MCNearestMeanClassifierSSLR: General Interface for MCNearestMeanClassifier (Moment Constrained Semi-supervised Nearest Mean Classifier) model

Description

model from RSSL package Update the means based on the moment constraints as defined in Loog (2010). The means estimated using the labeled data are updated by making sure their weighted mean corresponds to the overall mean on all (labeled and unlabeled) data. Optionally, the estimated variance of the classes can be re-estimated after this update is applied by setting update_sigma to TRUE. To get the true nearest mean classifier, rather than estimate the class priors, set them to equal priors using, for instance prior=matrix(0.5,2).

Usage

MCNearestMeanClassifierSSLR(
  update_sigma = FALSE,
  prior = NULL,
  x_center = FALSE,
  scale = FALSE
)

Arguments

update_sigma

logical; Whether the estimate of the variance should be updated after the means have been updated using the unlabeled data

prior

matrix; Class priors for the classes

x_center

logical; Should the features be centered?

scale

logical; Should the features be normalized? (default: FALSE)

References

Loog, M., 2010. Constrained Parameter Estimation for Semi-Supervised Learning: The Case of the Nearest Mean Classifier. In Proceedings of the 2010 European Conference on Machine learning and Knowledge Discovery in Databases. pp. 291-304.

Examples

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

data(breast)

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

cls <- which(colnames(breast) == "Class")

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


m <- MCNearestMeanClassifierSSLR() %>% fit(Class ~ ., data = train)

#Accesing model from RSSL
model <- m$model

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