fs.relief: Feature Selection Using RELIEF Method

In mt: Metabolomics Data Analysis Toolbox

Feature Selection Using RELIEF Method

Description

Feature selection using RELIEF method.

Usage

  fs.relief(x,y, m=NULL, k=10, ...)

Arguments

x	A data frame or matrix of data set.
y	A factor or vector of class.
m	Number of instances to sample without replacement. Default is NULL which takes all instances for computation.
k	Number of nearest neighbours used to estimate feature relevance.
...	Arguments to pass to method (current ignore).

Details

This function implements the Relief algorithm's extension called ReliefF, which applies to multi-class problem and searches for k of its nearest neighbours from the same class, called hits, and also k nearest neighbours from each of the different classes, called misses.

Value

A list with components:

fs.rank	A vector of feature ranking scores.
fs.order	A vector of feature order from best to worst.
stats	A vector of measurements.

Author(s)

Wanchang Lin

References

Kira, K. and Rendel, L. (1992). The Feature Selection Problem: Traditional Methods and a new algorithm. Proc. Tenth National Conference on Artificial Intelligence, MIT Press, 129 - 134.

Kononenko, I., Simes, E., and Robnik-Sikonja, M. (1997). Overcoming the Myopia of Induction Learning Algorithms with RELIEFF. Applied Intelligence, Vol.7, 1, 39-55.

Kononenko, I. (1994) Estimating Attributes: Analysis and Extensions of RELIEF, European Conference on Machine Learning, Ed. Francesco Bergadano and Luc De Raedt, 171-182, Springer

Robnik-Sikonja, M. and Kononenko, I. (2003) Theoretical and Empirical Analysis of ReliefF and RReliefF, Machine Learning, 53, 23 - 69.

Examples

data(iris)
x <- subset(iris, select = -Species)
y <- iris$Species

fs <- fs.relief(x, y, m=20,k=10)

mt documentation built on June 22, 2024, 12:24 p.m.