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weka.ReliefFAttributeEval

weka.ReliefFAttributeEval

Visibility: public Uploaded 05-12-2015 by Guido Santegoeds Weka_3.7.13 0 runs
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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.

Parameters

ASpecify sigma value (used in an exp function to control how quickly weights for more distant instances decrease. Use in conjunction with -W. Sensible value=1/5 to 1/10 of the number of nearest neighbours. (Default = 2)
DSeed for randomly sampling instances. (Default = 1)default: 1
KNumber of nearest neighbours (k) used to estimate attribute relevances (Default = 10).default: 10
MSpecify the number of instances to sample when estimating attributes. If not specified, then all instances will be used.default: -1
WWeight nearest neighbours by distance

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