"How to make Feature selection by Consistency-based in rapidminer studio?"

LookkuyeeLookkuyee Member Posts: 1 Newbie
edited May 2019 in Help
I'm a student and I have to do project about Data mining and Consistency-based Feature selection. 
But I don't know how to make Feature selection by Consistency-based in rapidminer studio?
Then I come to ask more Question and thanks for help me 

Answers

  • SGolbertSGolbert RapidMiner Certified Analyst, Member Posts: 344 Unicorn

    It was hard to find, but the operator that you need is available through the Weka Extension. It's called Performance (Consistency).

    Here is a sample process using the Optimize Selection (Evolutionary) operator:
    <?xml version="1.0" encoding="UTF-8"?><process version="9.1.000"><br>  <context><br>    <input/><br>    <output/><br>    <macros/><br>  </context><br>  <operator activated="true" class="process" compatibility="6.0.002" expanded="true" name="Root" origin="GENERATED_TUTORIAL"><br>    <parameter key="logverbosity" value="init"/><br>    <parameter key="random_seed" value="2000"/><br>    <parameter key="send_mail" value="never"/><br>    <parameter key="notification_email" value=""/><br>    <parameter key="process_duration_for_mail" value="30"/><br>    <parameter key="encoding" value="SYSTEM"/><br>    <process expanded="true"><br>      <operator activated="true" class="retrieve" compatibility="9.1.000" expanded="true" height="68" name="Retrieve Titanic Training" width="90" x="112" y="34"><br>        <parameter key="repository_entry" value="//Samples/data/Titanic Training"/><br>      </operator><br>      <operator activated="true" class="optimize_selection_evolutionary" compatibility="9.1.000" expanded="true" height="103" name="Optimize Selection (Evolutionary)" origin="GENERATED_TUTORIAL" width="90" x="380" y="34"><br>        <parameter key="use_exact_number_of_attributes" value="false"/><br>        <parameter key="restrict_maximum" value="false"/><br>        <parameter key="min_number_of_attributes" value="1"/><br>        <parameter key="max_number_of_attributes" value="1"/><br>        <parameter key="exact_number_of_attributes" value="1"/><br>        <parameter key="initialize_with_input_weights" value="false"/><br>        <parameter key="population_size" value="5"/><br>        <parameter key="maximum_number_of_generations" value="30"/><br>        <parameter key="use_early_stopping" value="false"/><br>        <parameter key="generations_without_improval" value="2"/><br>        <parameter key="normalize_weights" value="true"/><br>        <parameter key="use_local_random_seed" value="false"/><br>        <parameter key="local_random_seed" value="1992"/><br>        <parameter key="user_result_individual_selection" value="false"/><br>        <parameter key="show_population_plotter" value="false"/><br>        <parameter key="plot_generations" value="10"/><br>        <parameter key="constraint_draw_range" value="false"/><br>        <parameter key="draw_dominated_points" value="true"/><br>        <parameter key="maximal_fitness" value="Infinity"/><br>        <parameter key="selection_scheme" value="tournament"/><br>        <parameter key="tournament_size" value="0.25"/><br>        <parameter key="start_temperature" value="1.0"/><br>        <parameter key="dynamic_selection_pressure" value="true"/><br>        <parameter key="keep_best_individual" value="true"/><br>        <parameter key="save_intermediate_weights" value="false"/><br>        <parameter key="intermediate_weights_generations" value="10"/><br>        <parameter key="p_initialize" value="0.5"/><br>        <parameter key="p_mutation" value="-1.0"/><br>        <parameter key="p_crossover" value="0.5"/><br>        <parameter key="crossover_type" value="uniform"/><br>        <process expanded="true"><br>          <operator activated="true" class="split_validation" compatibility="9.1.000" expanded="true" height="124" name="Validation" origin="GENERATED_TUTORIAL" width="90" x="313" y="30"><br>            <parameter key="create_complete_model" value="false"/><br>            <parameter key="split" value="relative"/><br>            <parameter key="split_ratio" value="0.7"/><br>            <parameter key="training_set_size" value="100"/><br>            <parameter key="test_set_size" value="-1"/><br>            <parameter key="sampling_type" value="automatic"/><br>            <parameter key="use_local_random_seed" value="false"/><br>            <parameter key="local_random_seed" value="1992"/><br>            <process expanded="true"><br>              <operator activated="true" class="naive_bayes" compatibility="9.1.000" expanded="true" height="82" name="Naive Bayes" width="90" x="179" y="34"><br>                <parameter key="laplace_correction" value="true"/><br>              </operator><br>              <connect from_port="training" to_op="Naive Bayes" to_port="training set"/><br>              <connect from_op="Naive Bayes" from_port="model" to_port="model"/><br>              <portSpacing port="source_training" spacing="0"/><br>              <portSpacing port="sink_model" spacing="0"/><br>              <portSpacing port="sink_through 1" spacing="0"/><br>            </process><br>            <process expanded="true"><br>              <operator activated="true" class="apply_model" compatibility="7.1.001" expanded="true" height="82" name="Apply Model" origin="GENERATED_TUTORIAL" width="90" x="45" y="30"><br>                <list key="application_parameters"/><br>                <parameter key="create_view" value="false"/><br>              </operator><br>              <operator activated="true" class="weka:performance_consistency" compatibility="7.3.000" expanded="true" height="82" name="Performance (2)" width="90" x="246" y="34"/><br>              <connect from_port="model" to_op="Apply Model" to_port="model"/><br>              <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/><br>              <connect from_op="Apply Model" from_port="labelled data" to_op="Performance (2)" to_port="example set"/><br>              <connect from_op="Performance (2)" from_port="performance" to_port="averagable 1"/><br>              <portSpacing port="source_model" spacing="0"/><br>              <portSpacing port="source_test set" spacing="0"/><br>              <portSpacing port="source_through 1" spacing="0"/><br>              <portSpacing port="sink_averagable 1" spacing="0"/><br>              <portSpacing port="sink_averagable 2" spacing="0"/><br>            </process><br>          </operator><br>          <connect from_port="example set" to_op="Validation" to_port="training"/><br>          <connect from_op="Validation" from_port="averagable 1" to_port="performance"/><br>          <portSpacing port="source_example set" spacing="0"/><br>          <portSpacing port="source_through 1" spacing="0"/><br>          <portSpacing port="sink_performance" spacing="36"/><br>        </process><br>      </operator><br>      <connect from_op="Retrieve Titanic Training" from_port="output" to_op="Optimize Selection (Evolutionary)" to_port="example set in"/><br>      <connect from_op="Optimize Selection (Evolutionary)" from_port="example set out" to_port="result 1"/><br>      <connect from_op="Optimize Selection (Evolutionary)" from_port="performance" to_port="result 2"/><br>      <portSpacing port="source_input 1" spacing="0"/><br>      <portSpacing port="sink_result 1" spacing="0"/><br>      <portSpacing port="sink_result 2" spacing="18"/><br>      <portSpacing port="sink_result 3" spacing="0"/><br>    </process><br>  </operator><br></process><br><br>

    Regards,
    Sebastian

    sgenzerLookkuyee
Sign In or Register to comment.