Package

weka.attributeSelection

Synopsis

ReliefFAttributeEval :

Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class. Can operate on both discrete and continuous class data.

For more information see:

Kenji Kira, Larry A. Rendell: A Practical Approach to Feature Selection. In: Ninth International Workshop on Machine Learning, 249-256, 1992.

Igor Kononenko: Estimating Attributes: Analysis and Extensions of RELIEF. In: European Conference on Machine Learning, 171-182, 1994.

Marko Robnik-Sikonja, Igor Kononenko: An adaptation of Relief for attribute estimation in regression. In: Fourteenth International Conference on Machine Learning, 296-304, 1997.

Options

The table below describes the options available for ReliefFAttributeEval.

Option	Description
numNeighbours	Number of nearest neighbours for attribute estimation.
sampleSize	Number of instances to sample. Default (-1) indicates that all instances will be used for attribute estimation.
seed	Random seed for sampling instances.
sigma	Set influence of nearest neighbours. Used in an exp function to control how quickly weights decrease for more distant instances. Use in conjunction with weightByDistance. Sensible values = 1/5 to 1/10 the number of nearest neighbours.
weightByDistance	Weight nearest neighbours by their distance.

Capabilities

The table below describes the capabilites of ReliefFAttributeEval.

Capability	Supported
Class	Nominal class, Numeric class, Binary class, Missing class values, Date class
Attributes	Missing values, Date attributes, Empty nominal attributes, Nominal attributes, Binary attributes, Unary attributes, Numeric attributes
Min # of instances	1