computeSupervised: Supervised classification

In RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

Supervised classification

Description

Perform supervised classification based on the use of a training set.

Usage

computeSupervised(
  data.sample,
  prototypes,
  method.name = "K-NN",
  model = NULL,
  RclusTool.env = initParameters()
)

Arguments

data.sample	list containing features, profiles and clustering results.
prototypes	data.frame containing the features of each prototype associated to a class.
method.name	character vector specifying the supervised algorithm to use. Must be 'K-NN' (K-Nearest Neighbor by default), 'MLP' (MultiLayer Perceptron), 'SVM' (Support Vector Machine) or 'RF' (Random Forest).
model	option to predict directly from model
RclusTool.env	environment in which all global parameters, raw data and results are stored.

Details

computeSupervised performs supervised classification based on the use of a training set

Value

The function returns a list containing:

label	vector of labels.
summary	data.frame containing classes summaries (min, max, sum, average, sd).
nbItems	number of observations.
prototypes	data.frame containing the features of each prototype associated to a class.

See Also

readTrainSet

Examples

rep <- system.file("extdata", package="RclusTool")
featuresFile <- file.path(rep, "sample_example_features.csv")
features <- read.csv(featuresFile, header = TRUE)
features$ID <- NULL
traindir <- file.path(rep, "train_example")
tf <- tempfile()
write.table(features, tf, sep=",", dec=".")

x <- importSample(file.features=tf, dir.save=dirname(tf))

train <- readTrainSet(traindir)

res <- computeSupervised(x, prototypes=train)

plot(features[,3], features[,4], type = "p", xlab = "x", ylab = "y", 
col = res$label, main = "K-Nearest-Neighbor classification")

RclusTool documentation built on May 29, 2024, 5:23 a.m.