Description Usage Arguments Details Value Author(s) References Examples
The algorithm finds weights of continous and discrete attributes basing on a distance between instances.
1 |
formula |
a symbolic description of a model |
data |
data to process |
neighbours.count |
number of neighbours to find for every sampled instance |
sample.size |
number of instances to sample |
The algorithm samples instances and finds their nearest hits and misses. Considering that result, it evaluates weights of attributes.
a data.frame containing the worth of attributes in the first column and their names as row names
Piotr Romanski
-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.
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