"Weighted Examples do not work out?"

xxhasan88xx

Hi everyone,
i have a problem with class unbalanced data and example weighting. I have a dataset with 184 positive examples and 2200 negative ones. I know that there exist some solutions for that (e.g. sampling, weight attribute generation, cost-sensitive learning etc.).

I generate an attribut "weight" with the operator "Generate Weight (Stratification)" which assigns weights to all examples. However, this does not change anything in my results! My decision tree is the same as before. This problem exists also for Rule-Learners. Furthermore, this problem also exists if I generate a weight attribute manually (with functional expressions).

However, if i take a decision tree from the Weka-Extension (e.g. W-J48), it works and the tree seems to apply the example weights.

Now my question is, why doesn't the rapidminer decision tree seem to handle the example weights? What am I doing wrong? Whatever weights I generate, they do not work.

Thank you in advance.

Here you can see my process.

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fras

The weight attributes have a special role. This is the reason why the rapidminer tree ignores them.
Use operator "Set Role" to change the role to "regular".

xxhasan88xx

Thanks but unfortunately this is not the solution because

1) I want the weight attribute to be the weight (and not to be a regular attribute)
The Rapidminer operators just don't apply the weights.

2) the "Generate Weight (Stratification)" Operator automatically sets the role to "weight" and this is what i need.

3) Nevertheless if I use "set Role" to "weight", the problem still exists :-\

xxhasan88xx

Has nobody a solution?

Can at least anybody confirm that he/she has successfully used weighted examples with Rapidminer Operators?

mafern76

Are you sure?

For me, Decision Tree (Parallel) seems to be using weights.

Why did you set total weight at 10000? Could this be causing something? I just left it at 1.

MartinLiebig

i frequently use weights. At least in 6.X there is no problem i know of.

Cheers
Martin