classification based on the attribute having multiple values

sukh

Dear All,

i have 3 columns in my example set. these columns are Review(Text), Temporal_Tag(polynomial) and id(integer). the column temporal_tag contains 3 values past/present or future. it is set as label using set role operator. i tried various models to classify the given data set and further used the model after training it.
in short in want to get the performance of model in terms of precision and recall for temporal_tag.it has 3 classes.
present
past
future

Kindly help me in choosing the model.
Regards:
Sukh

MartinLiebig

have you tried Random Forest?

Best,
Martin

sukh

Respected Sir,
i have tried the following code using polynomial by binary classification;

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="6.3.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="6.0.002" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="6.3.000" expanded="true" height="60" name="Retrieve sentim20k" width="90" x="45" y="120">
        <parameter key="repository_entry" value="//Local Repository/Temp'training"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="6.3.000" expanded="true" height="76" name="Set Role" width="90" x="179" y="255">
        <parameter key="attribute_name" value="TempTag"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="polynomial_by_binomial_classification" compatibility="6.3.000" expanded="true" height="76" name="Polynomial by Binomial Classification" width="90" x="246" y="75">
        <parameter key="classification_strategies" value="1 against 1"/>
        <process expanded="true">
          <operator activated="true" class="x_validation" compatibility="5.1.002" expanded="true" name="Validation">
            <description>A cross-validation evaluating a decision tree model.</description>
            <parameter key="leave_one_out" value="true"/>
            <process expanded="true">
              <operator activated="true" class="support_vector_machine_libsvm" compatibility="6.3.000" expanded="true" name="SVM">
                <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="6.3.000" expanded="true" name="Apply Model">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance" compatibility="6.3.000" expanded="true" name="Performance"/>
              <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="6.3.000" expanded="true" height="76" name="Apply Model (2)" width="90" x="380" y="120">
        <list key="application_parameters"/>
      </operator>
      <operator activated="true" class="performance" compatibility="6.3.000" expanded="true" height="76" name="Performance (3)" width="90" x="514" y="30"/>
      <connect from_op="Retrieve sentim20k" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" 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 (3)" to_port="labelled data"/>
      <connect from_op="Performance (3)" from_port="performance" 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>

After this i get the error as SVM cannot handle polynomial label.
Regards:
Sukh