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 |