How to use Binary2MultiClassLearner

chenUser4321

Hi:

I want to wrote code to do multi-label classification. However, many of the existig RM classifiers only support binominal label. Binary2MultiClassLearner seems to be reasonable choice that makes these classifiers do multi-label classification.

However, I could not figure out a clear way to write the codes from rapidminer-4.6-tutorial.pdf and the javadoc. I also did some search in RM discussion forum, but found results are not directly related to development.

Could I have some specific instructions of using this learner? A sample java code that shows its usage is greatly appreciated!

I also posted this topic in the development forum. If it is not proper to post here, I will remove it.

Kindly regards,
Daozheng

Andrew2

Hello Daozheng,

I've used the operator "Polynomial by Binomial Classification" to do this sort of thing. Here's an example showing an SVM working on a three class problem.

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.0.10" expanded="true" name="Process">
    <process expanded="true" height="431" width="748">
      <operator activated="true" class="generate_data" compatibility="5.0.10" expanded="true" height="60" name="Generate Data" width="90" x="45" y="30">
        <parameter key="target_function" value="sum"/>
      </operator>
      <operator activated="true" class="discretize_by_user_specification" compatibility="5.0.10" expanded="true" height="94" name="Discretize" width="90" x="179" y="120">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="label"/>
        <parameter key="include_special_attributes" value="true"/>
        <list key="classes">
          <parameter key="first" value="-10.0"/>
          <parameter key="second" value="0.0"/>
          <parameter key="third" value="10.0"/>
        </list>
      </operator>
      <operator activated="true" class="polynomial_by_binomial_classification" compatibility="5.0.10" expanded="true" height="76" name="Polynomial by Binomial Classification" width="90" x="313" y="210">
        <process expanded="true" height="682" width="783">
          <operator activated="true" class="x_validation" compatibility="5.0.0" expanded="true" height="112" name="Validation" width="90" x="112" y="30">
            <description>A cross-validation evaluating a decision tree model.</description>
            <parameter key="leave_one_out" value="true"/>
            <process expanded="true" height="654" width="466">
              <operator activated="true" class="support_vector_machine_libsvm" compatibility="5.0.10" expanded="true" height="76" name="SVM" width="90" x="188" 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="654" width="466">
              <operator activated="true" class="apply_model" compatibility="5.0.0" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance" compatibility="5.0.0" 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_port="training set" to_op="Validation" to_port="training"/>
          <connect from_op="Validation" from_port="model" to_port="model"/>
          <portSpacing port="source_training set" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="apply_model" compatibility="5.0.10" expanded="true" height="76" name="Apply Model (2)" width="90" x="447" y="300">
        <list key="application_parameters"/>
      </operator>
      <operator activated="true" class="performance" compatibility="5.0.10" expanded="true" height="76" name="Performance (2)" width="90" x="514" y="30"/>
      <connect from_op="Generate Data" from_port="output" to_op="Discretize" to_port="example set input"/>
      <connect from_op="Discretize" from_port="example set output" to_op="Polynomial by Binomial Classification" to_port="training set"/>
      <connect from_op="Polynomial by Binomial Classification" from_port="model" to_op="Apply Model (2)" to_port="model"/>
      <connect from_op="Polynomial by Binomial Classification" from_port="example set" to_op="Apply Model (2)" to_port="unlabelled data"/>
      <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance (2)" to_port="labelled data"/>
      <connect from_op="Apply Model (2)" from_port="model" to_port="result 3"/>
      <connect from_op="Performance (2)" from_port="performance" to_port="result 1"/>
      <connect from_op="Performance (2)" from_port="example set" 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"/>
      <portSpacing port="sink_result 4" spacing="0"/>
    </process>
  </operator>
</process>

I'm no expert on Java code  but maybe this will give you a place to start.

regards

Andrew

chenUser4321

Thank you very much and sorry for the late reply! I will to understand this example and see how write the java code.

Daozheng

Seyhan

Hi,

I try to run multiclass classification(10 classes in the target attribute) with 10 fold X-val of below example, but it did not work.

Am I missing anything? It used to be Binary2MultiClassLearner in previous community versions but not with the version 5.

Regards,

Seyhan