"SVM Models - Target Variable with more than two levels"

HamsterDR

I have a data mining problem in which there are four levels in the target variable.  I have used a SVM model in Statistica that works very well for my data - and supports the four level target variable.  I am just starting out with Rapid Miner, and it looks like all the SVM models in Rapid Miner only support binary target variables.  Is that the case?  I think the libSVM implementation supports more than two levels (that is what Statistica uses) - but the description of this SVM implementation in Rapid Miner still seems to say that it only supports binary target variables.  If this capability is not available now, is it planned for the future?

David

Andrew2

Hello

It works fine with labels that have multiple nominal values.

Here's an example using the Iris data set

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.007">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.3.007" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="5.3.007" expanded="true" height="60" name="Retrieve Iris" width="90" x="112" y="75">
        <parameter key="repository_entry" value="//Samples/data/Iris"/>
      </operator>
      <operator activated="true" class="x_validation" compatibility="5.3.007" expanded="true" height="112" name="Validation" width="90" x="313" y="75">
        <process expanded="true">
          <operator activated="true" class="support_vector_machine_libsvm" compatibility="5.3.007" expanded="true" height="76" name="SVM" width="90" x="179" y="30">
            <list key="class_weights"/>
          </operator>
          <connect from_port="training" to_op="SVM" to_port="training set"/>
          <connect from_op="SVM" from_port="model" to_port="model"/>
          <portSpacing port="source_training" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
        </process>
        <process expanded="true">
          <operator activated="true" class="apply_model" compatibility="5.3.007" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="performance" compatibility="5.3.007" expanded="true" height="76" name="Performance" width="90" x="179" y="30"/>
          <connect from_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
          <connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
          <portSpacing port="source_model" spacing="0"/>
          <portSpacing port="source_test set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="sink_averagable 1" spacing="0"/>
          <portSpacing port="sink_averagable 2" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve Iris" from_port="output" to_op="Validation" to_port="training"/>
      <connect from_op="Validation" from_port="averagable 1" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>

regards

Andrew