swh:1:snp:0f920b1e114986636ba2e45b5c1a83473fb6cf12
Tip revision: 893e2d9
relief.Rd
\name{relief}
\alias{relief}
\title{ RReliefF filter }
\description{
The algorithm finds weights of continous and discrete attributes basing on a distance between instances.
}
\usage{
relief(formula, data, neighbours.count = 5, sample.size = 10)
}
\arguments{
\item{formula}{ a symbolic description of a model }
\item{data}{ data to process }
\item{neighbours.count}{ number of neighbours to find for every sampled instance }
\item{sample.size}{ number of instances to sample }
}
\details{
The algorithm samples instances and finds their nearest hits and misses. Considering that result, it evaluates weights of attributes.
}
\references{
\itemize{
\item{-}{Igor Kononenko: Estimating Attributes: Analysis and Extensions of RELIEF. In: European Conference on Machine Learning, 171-182, 1994.}
\item{-}{Marko Robnik-Sikonja, Igor Kononenko: An adaptation of Relief for attribute estimation in regression. In: Fourteenth International Conference on Machine Learning, 296-304, 1997.}
}
}
\value{
a data.frame containing the worth of attributes in the first column and their names as row names
}
\author{ Piotr Romanski }
\examples{
data(iris)

weights <- relief(Species~., iris, neighbours.count = 5, sample.size = 20)
print(weights)
subset <- cutoff.k(weights, 2)
f <- as.simple.formula(subset, "Species")
print(f)
}