Question: MetaLearning

Q-Dog

Hi everyone,

I've got a question regarding Metalearning (MetaCost, Bagging, Boosting,...).

Let's assume I've the following process:

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.017">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.1.017" expanded="true" name="Process">
    <process expanded="true" height="662" width="1095">
      <operator activated="true" class="retrieve" compatibility="5.1.017" expanded="true" height="60" name="Retrieve" width="90" x="45" y="120">
        <parameter key="repository_entry" value="//Samples/data/Iris"/>
      </operator>
      <operator activated="true" class="split_data" compatibility="5.1.017" expanded="true" height="94" name="Split Data" width="90" x="246" y="165">
        <enumeration key="partitions">
          <parameter key="ratio" value="0.9"/>
          <parameter key="ratio" value="0.1"/>
        </enumeration>
      </operator>
      <operator activated="true" class="metacost" compatibility="5.1.017" expanded="true" height="76" name="MetaCost" width="90" x="380" y="30">
        <parameter key="cost_matrix" value="[0.0 1.0 1.0;1.0 0.0 1.0;1.0 1.0 0.0]"/>
        <process expanded="true" height="662" width="1095">
          <operator activated="true" class="decision_tree" compatibility="5.1.017" expanded="true" height="76" name="Decision Tree" width="90" x="246" y="30"/>
          <connect from_port="training set" to_op="Decision Tree" to_port="training set"/>
          <connect from_op="Decision Tree" 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="5.1.017" expanded="true" height="76" name="Apply Model" width="90" x="581" y="120">
        <list key="application_parameters"/>
      </operator>
      <connect from_op="Retrieve" from_port="output" to_op="Split Data" to_port="example set"/>
      <connect from_op="Split Data" from_port="partition 1" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="Split Data" from_port="partition 2" to_op="MetaCost" to_port="training set"/>
      <connect from_op="MetaCost" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="Apply Model" from_port="labelled data" to_port="result 2"/>
      <connect from_op="Apply Model" from_port="model" 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"/>
      <portSpacing port="sink_result 3" spacing="0"/>
    </process>
  </operator>
</process>

In the result view you see 10 different decision trees (based on the 10 iterations in MetaCost). My question is, which of the 10 different trees will be used as a model for classification purposes? The last one?

If so, am I correct that the models are kind of evolving of the predecessor tree and the last one is the best?

Cheers Q-Dog

Nils_Woehler

Hi,

meta learning tries not to create the "best" classifier but is used in conjunction with many other learning algorithms to improve their performance.This can be achieved by for example use the results of all created classifiers to train a new one or by use a majority voting on all trained classifiers.
How MetaCost works exactly can be seen here http://goo.gl/H8fuH

Greetings Nils