"Calculate macro- and micro-averaged f-measure on multiclass data"

Hello,

I was wondering if RapidMiner has the ability to calculate the macro- and micro-averaged f-measure for multiclass data (i.e., more than two classes).  I know that when I work with binomial data the Binomial Classification Performance operator has an option for f-measure.  However, the multiclass version of the Performance operator does not calculate f-measure (as far as I can tell).

As an alternative, is there a way to capture the confusion matrix from the Performance operator (for logging purposes)?  Again, I see that the binomial version of the operator has this capability, but I don't see such options in the multiclass version.

Below is a sample of my process.

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.004">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.1.004" expanded="true" name="Process">
    <process expanded="true" height="591" width="752">
      <operator activated="true" class="generate_data" compatibility="5.1.004" expanded="true" height="60" name="Generate Data" width="90" x="37" y="30">
        <parameter key="target_function" value="multi classification"/>
      </operator>
      <operator activated="true" class="x_validation" compatibility="5.1.004" expanded="true" height="112" name="Validation" width="90" x="179" y="30">
        <process expanded="true" height="591" width="351">
          <operator activated="true" class="support_vector_machine_libsvm" compatibility="5.1.004" expanded="true" height="76" name="SVM" width="90" x="112" 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" height="591" width="351">
          <operator activated="true" class="apply_model" compatibility="5.1.004" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="performance_classification" compatibility="5.1.004" expanded="true" height="76" name="Performance" width="90" x="179" y="30">
            <parameter key="classification_error" value="true"/>
            <parameter key="kappa" value="true"/>
            <parameter key="weighted_mean_recall" value="true"/>
            <parameter key="weighted_mean_precision" value="true"/>
            <parameter key="spearman_rho" value="true"/>
            <parameter key="kendall_tau" value="true"/>
            <parameter key="absolute_error" value="true"/>
            <parameter key="relative_error" value="true"/>
            <parameter key="relative_error_lenient" value="true"/>
            <parameter key="relative_error_strict" value="true"/>
            <parameter key="normalized_absolute_error" value="true"/>
            <parameter key="root_mean_squared_error" value="true"/>
            <parameter key="root_relative_squared_error" value="true"/>
            <parameter key="squared_error" value="true"/>
            <parameter key="correlation" value="true"/>
            <parameter key="squared_correlation" value="true"/>
            <parameter key="cross-entropy" value="true"/>
            <parameter key="margin" value="true"/>
            <parameter key="soft_margin_loss" value="true"/>
            <parameter key="logistic_loss" value="true"/>
            <list key="class_weights"/>
          </operator>
          <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="Generate Data" from_port="output" to_op="Validation" to_port="training"/>
      <connect from_op="Validation" from_port="training" to_port="result 1"/>
      <connect from_op="Validation" from_port="averagable 1" 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"/>
    </process>
  </operator>
</process>

Any help would be greatly appreciated.

Thanks!