如何在RapidMiner中降低文本维度

Question

我在使用Rapid Miner来降低文本挖掘的特征维度方面遇到了挑战。在这一点上，我正在通过单词标记来处理文本，这导致了一个非常大的维度集，这对于建模和预测并不理想。我如何改进流程，以使用其他方法清理数据，并只接受相关的单词？

我尝试过应用tfidf，但它删除了target变量，并且在模型阶段之前我看不到它做了什么。

谢谢

Answer 1

Process Documents操作符有一个修剪选项，在这个选项中，通过仔细设置参数，您可以删除常见和罕见的属性。

这里有一个玩具示例来说明它的工作原理。

<?xml version="1.0" encoding="UTF-8"?><process version="7.5.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.5.000" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="text:create_document" compatibility="7.4.001" expanded="true" height="68" name="Create Document" width="90" x="179" y="187">
        <parameter key="text" value="the cat sat on the mat&#10;the dog barked at the man&#10;the cow ate the grass&#10;the man sat on the grass"/>
      </operator>
      <operator activated="true" class="text:create_document" compatibility="7.4.001" expanded="true" height="68" name="Create Document (2)" width="90" x="179" y="289">
        <parameter key="text" value="the cat sat on the mat&#10;the man sat on the grass&#10;the rain in spain falls mainly on the plain"/>
      </operator>
      <operator activated="true" class="text:create_document" compatibility="7.4.001" expanded="true" height="68" name="Create Document (3)" width="90" x="179" y="391">
        <parameter key="text" value="the world is round"/>
      </operator>
      <operator activated="true" class="text:process_documents" compatibility="7.4.001" expanded="true" height="145" name="Process Documents" width="90" x="447" y="187">
        <parameter key="vector_creation" value="Term Occurrences"/>
        <parameter key="prune_method" value="absolute"/>
        <parameter key="prune_above_percent" value="40.0"/>
        <parameter key="prune_below_absolute" value="2"/>
        <parameter key="prune_above_absolute" value="5"/>
        <process expanded="true">
          <operator activated="true" class="text:tokenize" compatibility="7.4.001" expanded="true" height="68" name="Tokenize" width="90" x="246" y="34"/>
          <connect from_port="document" to_op="Tokenize" to_port="document"/>
          <connect from_op="Tokenize" from_port="document" to_port="document 1"/>
          <portSpacing port="source_document" spacing="0"/>
          <portSpacing port="sink_document 1" spacing="0"/>
          <portSpacing port="sink_document 2" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Create Document" from_port="output" to_op="Process Documents" to_port="documents 1"/>
      <connect from_op="Create Document (2)" from_port="output" to_op="Process Documents" to_port="documents 2"/>
      <connect from_op="Create Document (3)" from_port="output" to_op="Process Documents" to_port="documents 3"/>
      <connect from_op="Process Documents" from_port="example set" 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>

它需要一些关注，以使其恰到好处，但希望这将帮助您开始。