【问题标题】:How to select the records depend on PCs to reduce dimensionality in Rapidminer?Rapidminer中如何选择依赖PC的记录来降维?
【发布时间】:2019-02-16 12:17:23
【问题描述】:

我是 Rapidminer 的新手,所以我有一个庞大的数据集,我使用主成分分析来降低维度,问题是当我拿到 PC 时我不知道如何选择记录取决于它我该如何制作一个减少的新数据集?

这是我尝试使用的:

这就是我得到的:

【问题讨论】:

    标签: pca rapidminer dimensionality-reduction


    【解决方案1】:

    您可以使用“PCA 加权”运算符计算属性重要性的权重,然后使用“按权重选择”运算符减少原始数据集中的属性数量。

    检查下面附加的示例流程(只需将 XML 复制到您的 RapidMiner 流程​​窗口中)。 也可以随时查看或提问RapidMiner community

    <?xml version="1.0" encoding="UTF-8"?><process version="9.2.000">
    <context>
    <input/>
    <output/>
    <macros/>
    </context>
    <operator activated="true" class="process" compatibility="6.0.002" expanded="true" name="Root" origin="GENERATED_TUTORIAL">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="9.2.000" expanded="true" height="68" name="Sonar" origin="GENERATED_TUTORIAL" width="90" x="112" y="34">
        <parameter key="repository_entry" value="//Samples/data/Sonar"/>
      </operator>
      <operator activated="true" class="weight_by_pca" compatibility="9.2.000" expanded="true" height="82" name="Weight by PCA" width="90" x="313" y="34">
        <parameter key="normalize_weights" value="true"/>
        <parameter key="sort_weights" value="true"/>
        <parameter key="sort_direction" value="ascending"/>
        <parameter key="component_number" value="1"/>
      </operator>
      <operator activated="true" class="select_by_weights" compatibility="9.2.000" expanded="true" height="103" name="Select by Weights" width="90" x="581" y="34">
        <parameter key="weight_relation" value="greater equals"/>
        <parameter key="weight" value="0.5"/>
        <parameter key="k" value="10"/>
        <parameter key="p" value="0.5"/>
        <parameter key="deselect_unknown" value="true"/>
        <parameter key="use_absolute_weights" value="true"/>
      </operator>
      <connect from_op="Sonar" from_port="output" to_op="Weight by PCA" to_port="example set"/>
      <connect from_op="Weight by PCA" from_port="weights" to_op="Select by Weights" to_port="weights"/>
      <connect from_op="Weight by PCA" from_port="example set" to_op="Select by Weights" to_port="example set input"/>
      <connect from_op="Select by Weights" from_port="example set output" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="162"/>
    </process>
    </operator>
    </process>
    

    【讨论】:

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