normalizedRelief: Normalized Relief

In FSinR: Feature Selection

Description

Generates an evaluation function that calculates a measure of the set of features between 0 and 1 with relief (individual measure). The relief algorithm \insertCiteKira1992FSinR finds weights of continous and discrete attributes basing on a distance between instances. Adapted from Piotr Romanski's Fselector package \insertCiteFSelectorPkgFSinR. This function is called internally within the filterEvaluator function.

Usage

normalizedRelief(neighbours.count = 5, sample.size = 10)

Arguments

neighbours.count	number of neighbours to find for every sampled instance
sample.size	number of instances to sample

Details

relief classification and regression continous and discrete data

Value

Returns a function that is used to generate an individual evaluation measure using relief

Author(s)

Alfonso Jiménez-Vílchez

References

\insertAllCited

Examples

## Not run:  

## The direct application of this function is an advanced use that consists of using this 
# function directly to individually evaluate a set of features
## Classification problem

# Generate the evaluation function with Cramer
relief_evaluator <- normalizedRelief()
# Evaluate the features (parameters: dataset, target variable and features)
relief_evaluator(iris,'Species',c('Sepal.Length'))

## End(Not run)

FSinR documentation built on Nov. 23, 2020, 5:10 p.m.