normalizedRelief {FSinR}

Normalized Relief

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 (Kira and Rendell 1992) finds weights of continous and discrete attributes basing on a distance between instances. Adapted from Piotr Romanski's Fselector package (Romanski and Kotthoff 2018). 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

Kira K, Rendell LA (1992). “A practical approach to feature selection.” In Machine Learning Proceedings 1992, 249–256. Elsevier.

Romanski P, Kotthoff L (2018). FSelector: Selecting Attributes. R package version 0.31, https://CRAN.R-project.org/package=FSelector.

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)