"PCA for 101010101 series prediction"

Dear All,

I have a process which predicts the next Boolean value given a Boolean series.

The processes first applies windowing.
Then a sliding window validation is ran.
Inside the sliding window validation PCA is applied.
After this integer {1, 0} values are converted Boolean.
And then a J48 learner is applied.
Before apply model  {1, 0} values are converted yet again converted to Boolean.

This constantly converting between Boolean to integer makes the processes really slow!
Is there a way to overcome this problem?
Can we apply PCA to a Boolean data set?

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">
    <parameter key="parallelize_main_process" value="true"/>
    <process expanded="true" height="445" width="435">
      <operator activated="true" class="retrieve" compatibility="5.1.006" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30">
        <parameter key="repository_entry" value="DNA"/>
      </operator>
      <operator activated="true" class="series:windowing" compatibility="5.1.002" expanded="true" height="76" name="Windowing" width="90" x="180" y="30">
        <parameter key="horizon" value="1"/>
        <parameter key="create_label" value="true"/>
        <parameter key="label_attribute" value="x"/>
      </operator>
      <operator activated="true" class="series:sliding_window_validation" compatibility="5.1.002" expanded="true" height="112" name="Validation" width="90" x="315" y="30">
        <parameter key="training_window_step_size" value="100"/>
        <parameter key="test_window_width" value="1"/>
        <parameter key="average_performances_only" value="false"/>
        <parameter key="parallelize_training" value="true"/>
        <parameter key="parallelize_testing" value="true"/>
        <process expanded="true" height="445" width="435">
          <operator activated="true" class="principal_component_analysis" compatibility="5.1.006" expanded="true" height="94" name="PCA" width="90" x="45" y="30"/>
          <operator activated="true" class="numerical_to_binominal" compatibility="5.1.006" expanded="true" height="76" name="Numerical to Binominal" width="90" x="180" y="30">
            <parameter key="include_special_attributes" value="true"/>
          </operator>
          <operator activated="true" class="weka:W-J48" compatibility="5.1.000" expanded="true" height="76" name="W-J48" width="90" x="315" y="30"/>
          <connect from_port="training" to_op="PCA" to_port="example set input"/>
          <connect from_op="PCA" from_port="example set output" to_op="Numerical to Binominal" to_port="example set input"/>
          <connect from_op="Numerical to Binominal" from_port="example set output" to_op="W-J48" to_port="training set"/>
          <connect from_op="W-J48" 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="445" width="435">
          <operator activated="true" class="numerical_to_binominal" compatibility="5.1.006" expanded="true" height="76" name="Numerical to Binominal (2)" width="90" x="45" y="75">
            <parameter key="include_special_attributes" value="true"/>
          </operator>
          <operator activated="true" class="apply_model" compatibility="5.1.006" expanded="true" height="76" name="Apply Model" width="90" x="180" y="30">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="performance_classification" compatibility="5.1.006" expanded="true" height="76" name="Performance (2)" width="90" x="315" y="30">
            <parameter key="accuracy" value="false"/>
            <parameter key="kappa" value="true"/>
            <list key="class_weights"/>
          </operator>
          <connect from_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_port="test set" to_op="Numerical to Binominal (2)" to_port="example set input"/>
          <connect from_op="Numerical to Binominal (2)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_op="Apply Model" from_port="labelled data" to_op="Performance (2)" to_port="labelled data"/>
          <connect from_op="Performance (2)" 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="Windowing" to_port="example set input"/>
      <connect from_op="Windowing" from_port="example set 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="36"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>