DSClassifier_SlidingWindow: DSClassifier_SlidingWindow - Data Stream Classifier Using a...

In stream: Infrastructure for Data Stream Mining

DSClassifier_SlidingWindow – Data Stream Classifier Using a Sliding Window

Description

The classifier keeps a sliding window for the stream and rebuilds a classification model at regular intervals. By default is builds a decision tree using rpart().

Usage

DSClassifier_SlidingWindow(formula, model = rpart::rpart, window, rebuild, ...)

Arguments

formula	a formula for the classification problem.
model	classifier model (that has a formula interface).
window	size of the sliding window.
rebuild	interval (number of points) for rebuilding the classifier. Set rebuild to Inf to prevent automatic rebuilding. Rebuilding can be initiated manually when calling update().
...	additional parameters are passed on to the classifier (default is rpart()).

Details

This constructor creates classifier based on DST_SlidingWindow. The classifier has a update() and predict() method.

Value

An object of class DST_SlidingWindow.

Author(s)

Michael Hahsler

See Also

Other DSClassifier: DSClassifier()

Examples

library(stream)

# create a data stream for the iris dataset
data <- iris[sample(nrow(iris)), ]
stream <- DSD_Memory(data)

# define the stream classifier.
cl <- DSClassifier_SlidingWindow(
  Species ~ Sepal.Length + Sepal.Width + Petal.Length,
  window = 50,
  rebuild = 10
  )
cl

# update the classifier with 100 points from the stream
update(cl, stream, 100)

# predict the class for the next 50 points
newdata <- get_points(stream, n = 50)
pr <- predict(cl, newdata, type = "class")
pr

table(pr, newdata$Species)

# get the tree model
get_model(cl)

stream documentation built on May 29, 2024, 9:43 a.m.