information.gain: Entropy-based filters

In FSelector: Selecting attributes

Description

The algorithms find weights of discrete attributes basing on their correlation with continous class attribute.

Usage

information.gain(formula, data)
gain.ratio(formula, data)
symmetrical.uncertainty(formula, data)

Arguments

formula	a symbolic description of a model
data	data to process

Details

information.gain is

H(Class) + H(Attribute) - H(Class, Attribute)

.

gain.ratio is

(H(Class) + H(Attribute) - H(Class, Attribute)) / H(Attribute)

symmetrical.uncertainty is

2 * (H(Class) + H(Attribute) - H(Class, Attribute)) / (H(Attribute) + H(Class))

Value

a data.frame containing the worth of attributes in the first column and their names as row names

Author(s)

Piotr Romanski

Examples

  data(iris)

  weights <- information.gain(Species~., iris)
  print(weights)
  subset <- cutoff.k(weights, 2)
  f <- as.simple.formula(subset, "Species")
  print(f)

  weights <- gain.ratio(Species~., iris)
  print(weights)
  subset <- cutoff.k(weights, 2)
  f <- as.simple.formula(subset, "Species")
  print(f)

  weights <- symmetrical.uncertainty(Species~., iris)
  print(weights)
  subset <- cutoff.biggest.diff(weights)
  f <- as.simple.formula(subset, "Species")
  print(f)

FSelector documentation built on May 2, 2019, 4:52 p.m.