Question

我有一个3级阳性，中性和阴性的数据集。我尝试使用SVM创建分类器。我的数据集： dataset:

我在Rapidminer中的代码：

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.015">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.3.015" expanded="true" name="Process">
    <parameter key="parallelize_main_process" value="true"/>
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="5.3.015" expanded="true" height="60" name="Retrieve sentim20k" width="90" x="45" y="210">
        <parameter key="repository_entry" value="//Local Repository/diploamitki/new/sentim20k"/>
      </operator>
      <operator activated="true" class="x_validation" compatibility="5.1.002" expanded="true" height="112" name="Validation" width="90" x="447" y="165">
        <description>A cross-validation evaluating a decision tree model.</description>
        <parameter key="parallelize_training" value="true"/>
        <parameter key="parallelize_testing" value="true"/>
        <process expanded="true">
          <operator activated="true" class="support_vector_machine" compatibility="5.3.015" expanded="true" height="112" name="SVM" width="90" x="112" y="30"/>
          <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="5.3.015" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="performance" compatibility="5.3.015" expanded="true" height="76" name="Performance" width="90" x="345" 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_op="Retrieve sentim20k" from_port="output" to_op="Validation" to_port="training"/>
      <connect from_op="Validation" from_port="averagable 1" 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>

我有这个错误： error

我知道SVM可以处理2个类，但是如何使用tis数据集创建模型？

Answer 1

我找到了解决方案。我使用了运算符＆＃34; Polynominal by Bionominal Classification＆＃34;。该操作员使用SVM训练3类模型。

这里有一个例子：

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.015">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.3.015" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="5.3.015" expanded="true" height="60" name="Retrieve sentim20k" width="90" x="45" y="120">
        <parameter key="repository_entry" value="//Local Repository/diploamitki/new/sentim20k"/>
      </operator>
      <operator activated="true" class="polynomial_by_binomial_classification" compatibility="5.3.015" 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="5.3.015" 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="5.3.015" expanded="true" name="Apply Model">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance" compatibility="5.3.015" 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="5.3.015" 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="5.3.015" expanded="true" height="76" name="Performance (3)" width="90" x="514" y="30"/>
      <connect from_op="Retrieve sentim20k" from_port="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>

SVM超过2级快速通道

1 个答案: