"Weighted Examples do not work?"

xxhasan88xxxxhasan88xx Member Posts: 4 Contributor I
edited June 5 in Help
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.).

Well, I want to 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). Furthermore, i used the "Set Role"-Operator to set the attribute as weight, this doesn't work either.

The interesting thing is that, 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, they do not affect the algorithm and the results.

Thank you in advance.

Here you can see my process.
<process version="5.3.015">
 <operator activated="true" class="process" compatibility="5.3.015" expanded="true" name="Process">
   <process expanded="true">
     <operator activated="true" class="retrieve" compatibility="5.3.015" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30">
       <parameter key="repository_entry" value="../MiningData/BaseMiningTable"/>
     <operator activated="true" class="generate_weight_stratification" compatibility="5.3.015" expanded="true" height="76" name="Generate Weight (2)" width="90" x="179" y="30">
       <parameter key="total_weight" value="10000.0"/>
     <operator activated="true" class="decision_tree" compatibility="5.3.015" expanded="true" height="76" name="Decision Tree (2)" width="90" x="313" y="30">
       <parameter key="minimal_leaf_size" value="5"/>
       <parameter key="minimal_gain" value="0.01"/>
       <parameter key="maximal_depth" value="5"/>
     <connect from_op="Retrieve" from_port="output" to_op="Generate Weight (2)" to_port="example set input"/>
     <connect from_op="Generate Weight (2)" from_port="example set output" to_op="Decision Tree (2)" to_port="training set"/>
     <connect from_op="Decision Tree (2)" from_port="model" to_port="result 1"/>
     <connect from_op="Decision Tree (2)" from_port="exampleSet" to_port="result 2"/>
     <portSpacing port="source_input 1" spacing="0"/>
     <portSpacing port="sink_result 1" spacing="0"/>
     <portSpacing port="sink_result 2" spacing="0"/>
     <portSpacing port="sink_result 3" spacing="0"/>
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