thank you for your answer.
Maybe some more explanation on the variable importance measure proposed by Breiman for his
First of all he defines out-of-bag (OOB) data as data which is not part of the bootstrap
sample. The bootstrap sample drawn form the original data in turn is used to grow N decision
trees. At each node, a randomly chosen number of attributes is taken to find the best split.
Now the variable importance measure comes into play. For each of the trees grown in the forest,
the OOB data is put down and the number of votes cast for the correct class is counted. Next,
the values of variable m in the OOB cases are randomly permuted and again these cases are
put down the tree. Finally, the number of votes for the correct class in the variable-m-permuted
OOB data is subtracted from the number of votes for the correct class in the first untouched OOB
data. Thus, the larger the difference, the more important this variable m is.The average of
the differences over all trees in the forest is defined as an importance of variabble m.
If you want to calculate how import a variable is outside these learning algorithms I suggest the operators "GiniIndexWeighting", "InfoGainWeighting" / "InfoGainRatioWeighting". I personally prefer "InfoGainRatio".
I don't think that the approaches you've suggested can be applied
to realize the variable importance measure as suggested to be
most appropriate by Breiman. As far as I can see, the Weighting
operators are independent of the used learner. But to achieve
what I described above requires an itegration of a weighting operator
into the RandomForest operator, i.e. while growing the forest, the
variable importance estimation must take place. Or am I wrong and
you see a way on how to get Breiman's approach working in RapidMiner?