Dear All,
Is it possible to run feature selection inside cross validation?
I created the following process (pasted below) which I believe does exactly this.
But I'm afraid that the remember and recall of feature weights might return feature weights from the previous loop.
Best regards,
Wessel
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.006">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.1.006" expanded="true" name="Process">
<process expanded="true" height="409" width="678">
<operator activated="true" class="retrieve" compatibility="5.1.006" expanded="true" height="60" name="Retrieve" width="90" x="112" y="75">
<parameter key="repository_entry" value="//Samples/data/Sonar"/>
</operator>
<operator activated="true" class="x_validation" compatibility="5.1.006" expanded="true" height="112" name="k-fold" width="90" x="246" y="75">
<parameter key="number_of_validations" value="144"/>
<process expanded="true" height="391" width="300">
<operator activated="true" class="weka:W-ReliefFAttributeEval" compatibility="5.1.000" expanded="true" height="76" name="W-ReliefFAttributeEval (2)" width="90" x="45" y="30"/>
<operator activated="true" class="select_by_weights" compatibility="5.1.006" expanded="true" height="94" name="Select by Weights (2)" width="90" x="180" y="30">
<parameter key="weight_relation" value="top k"/>
<parameter key="k" value="5"/>
</operator>
<operator activated="true" class="remember" compatibility="5.1.006" expanded="true" height="60" name="Remember" width="90" x="45" y="120">
<parameter key="name" value="f"/>
<parameter key="io_object" value="AttributeWeights"/>
</operator>
<operator activated="true" class="naive_bayes" compatibility="5.1.006" expanded="true" height="76" name="Naive Bayes" width="90" x="179" y="120"/>
<connect from_port="training" to_op="W-ReliefFAttributeEval (2)" to_port="example set"/>
<connect from_op="W-ReliefFAttributeEval (2)" from_port="weights" to_op="Select by Weights (2)" to_port="weights"/>
<connect from_op="W-ReliefFAttributeEval (2)" from_port="example set" to_op="Select by Weights (2)" to_port="example set input"/>
<connect from_op="Select by Weights (2)" from_port="example set output" to_op="Naive Bayes" to_port="training set"/>
<connect from_op="Select by Weights (2)" from_port="weights" to_op="Remember" to_port="store"/>
<connect from_op="Naive Bayes" 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" height="391" width="300">
<operator activated="true" class="recall" compatibility="5.1.006" expanded="true" height="60" name="Recall" width="90" x="45" y="30">
<parameter key="name" value="f"/>
<parameter key="io_object" value="AttributeWeights"/>
</operator>
<operator activated="true" class="select_by_weights" compatibility="5.1.006" expanded="true" height="94" name="Select by Weights (3)" width="90" x="180" y="30">
<parameter key="weight_relation" value="top k"/>
<parameter key="k" value="5"/>
</operator>
<operator activated="true" class="apply_model" compatibility="5.1.006" expanded="true" height="76" name="-1 Folds Model" width="90" x="45" y="120">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="5.1.006" expanded="true" height="76" name="Accuracy Fold" width="90" x="180" y="120">
<list key="class_weights"/>
</operator>
<connect from_port="model" to_op="-1 Folds Model" to_port="model"/>
<connect from_port="test set" to_op="Select by Weights (3)" to_port="example set input"/>
<connect from_op="Recall" from_port="result" to_op="Select by Weights (3)" to_port="weights"/>
<connect from_op="Select by Weights (3)" from_port="example set output" to_op="-1 Folds Model" to_port="unlabelled data"/>
<connect from_op="-1 Folds Model" from_port="labelled data" to_op="Accuracy Fold" to_port="labelled data"/>
<connect from_op="Accuracy Fold" 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" from_port="output" to_op="k-fold" to_port="training"/>
<connect from_op="k-fold" from_port="averagable 1" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="72"/>
<portSpacing port="sink_result 2" spacing="18"/>
</process>
</operator>
</process>
