SVM多于2 class rapidminer
SVM more than 2 class rapidminer
我有一个包含 3 class 正面、中性和负面的数据集。
我尝试使用 SVM 创建一个 classifier。
我的数据集：
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：
我知道 SVM 可以处理 2 class 但我如何使用 tis 数据集创建模型？
我找到了解决办法。我使用了运算符 "Polynominal by Bionominal Classification"。
此运算符使用 SVM 训练具有 3 class 的模型。
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>
我有一个包含 3 class 正面、中性和负面的数据集。
我尝试使用 SVM 创建一个 classifier。
我的数据集：
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：
我知道 SVM 可以处理 2 class 但我如何使用 tis 数据集创建模型？
我找到了解决办法。我使用了运算符 "Polynominal by Bionominal Classification"。 此运算符使用 SVM 训练具有 3 class 的模型。
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>