Classification by naive bayes

Ka13n

Dear Community

Good day and Merry Christmas!
I'm still new to this field and try to explore as much knowledge as I can now.... 
I've a question regarding using naive bayes for classification.

I got a data set with words, sum (in total) and sum (in document), for the further step I wanna use the text mining data to classified the paper which I collected.
However, my process keeps showing ''the example set is empty'', which can be referred to below XML. 
Is there any solution for the wrong output? If do, can any expert advise me? 
Thanks in advance and wish you all have a nice holiday!

--XML--

<?xml version="1.0" encoding="UTF-8"?><process version="9.5.001">

  <context>

    <input/>

    <output/>

    <macros/>

  </context>

  <operator activated="true" class="process" compatibility="9.5.001" expanded="true" name="Process">

    <parameter key="logverbosity" value="init"/>

    <parameter key="random_seed" value="2001"/>

    <parameter key="send_mail" value="never"/>

    <parameter key="notification_email" value=""/>

    <parameter key="process_duration_for_mail" value="30"/>

    <parameter key="encoding" value="SYSTEM"/>

    <process expanded="true">

      <operator activated="true" class="retrieve" compatibility="9.5.001" expanded="true" height="68" name="Retrieve Acceptance 100 rfef result" width="90" x="112" y="85">

        <parameter key="repository_entry" value="//Local Repository/new data test/Acceptance 100 rfef result"/>

      </operator>

      <operator activated="true" class="filter_examples" compatibility="9.5.001" expanded="true" height="103" name="Filter Examples" width="90" x="246" y="85">

        <parameter key="parameter_expression" value=""/>

        <parameter key="condition_class" value="custom_filters"/>

        <parameter key="invert_filter" value="false"/>

        <list key="filters_list">

          <parameter key="filters_entry_key" value="sum(in documents).ge.75"/>

        </list>

        <parameter key="filters_logic_and" value="true"/>

        <parameter key="filters_check_metadata" value="true"/>

      </operator>

      <operator activated="true" class="set_role" compatibility="9.5.001" expanded="true" height="82" name="Set Role" width="90" x="380" y="85">

        <parameter key="attribute_name" value="word"/>

        <parameter key="target_role" value="label"/>

        <list key="set_additional_roles"/>

      </operator>

      <operator activated="true" class="split_validation" compatibility="9.5.001" expanded="true" height="124" name="Validation" width="90" x="514" y="85">

        <parameter key="create_complete_model" value="false"/>

        <parameter key="split" value="relative"/>

        <parameter key="split_ratio" value="0.7"/>

        <parameter key="training_set_size" value="100"/>

        <parameter key="test_set_size" value="-1"/>

        <parameter key="sampling_type" value="automatic"/>

        <parameter key="use_local_random_seed" value="false"/>

        <parameter key="local_random_seed" value="1992"/>

        <process expanded="true">

          <operator activated="true" class="naive_bayes" compatibility="9.5.001" expanded="true" height="82" name="Naive Bayes" width="90" x="246" y="34">

            <parameter key="laplace_correction" value="true"/>

          </operator>

          <connect from_port="training" to_op="Naive Bayes" to_port="training set"/>

          <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">

          <operator activated="true" class="apply_model" compatibility="9.5.001" expanded="true" height="82" name="Apply Model" width="90" x="112" y="34">

            <list key="application_parameters"/>

            <parameter key="create_view" value="false"/>

          </operator>

          <operator activated="true" class="performance_classification" compatibility="9.5.001" expanded="true" height="82" name="Performance" width="90" x="380" y="34">

            <parameter key="main_criterion" value="first"/>

            <parameter key="accuracy" value="true"/>

            <parameter key="classification_error" value="false"/>

            <parameter key="kappa" value="false"/>

            <parameter key="weighted_mean_recall" value="false"/>

            <parameter key="weighted_mean_precision" value="false"/>

            <parameter key="spearman_rho" value="false"/>

            <parameter key="kendall_tau" value="false"/>

            <parameter key="absolute_error" value="false"/>

            <parameter key="relative_error" value="false"/>

            <parameter key="relative_error_lenient" value="false"/>

            <parameter key="relative_error_strict" value="false"/>

            <parameter key="normalized_absolute_error" value="false"/>

            <parameter key="root_mean_squared_error" value="false"/>

            <parameter key="root_relative_squared_error" value="false"/>

            <parameter key="squared_error" value="false"/>

            <parameter key="correlation" value="false"/>

            <parameter key="squared_correlation" value="false"/>

            <parameter key="cross-entropy" value="false"/>

            <parameter key="margin" value="false"/>

            <parameter key="soft_margin_loss" value="false"/>

            <parameter key="logistic_loss" value="false"/>

            <parameter key="skip_undefined_labels" value="true"/>

            <parameter key="use_example_weights" 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="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 Acceptance 100 rfef result" from_port="output" to_op="Filter Examples" to_port="example set input"/>

      <connect from_op="Filter Examples" from_port="example set output" to_op="Set Role" to_port="example set input"/>

      <connect from_op="Set Role" from_port="example set output" to_op="Validation" to_port="training"/>

      <connect from_op="Validation" from_port="model" to_port="result 1"/>

      <connect from_op="Validation" from_port="training" to_port="result 2"/>

      <connect from_op="Validation" from_port="averagable 1" to_port="result 3"/>

      <portSpacing port="source_input 1" spacing="0"/>

      <portSpacing port="sink_result 1" spacing="0"/>

      <portSpacing port="sink_result 2" spacing="0"/>

      <portSpacing port="sink_result 3" spacing="0"/>

      <portSpacing port="sink_result 4" spacing="0"/>

    </process>

  </operator>

</process>

sgenzer

hi @Ka13n can you also post your repository data set "Acceptance 100 rfef result"? We cannot run your process and replicate your result without it.

Scott

Ka13n

Dear Scott

Thanks for spending time on my process, I've PM you regarding the repository.
Please kindly find the data set and let me know if anything needed to solve the problem.

sgenzer

ok thank you I have your data set. So yeah...I can see why you have an empty set. You're trying to ask Naive Bayes to do classification where the target/label is different for every row. That's an impossible problem.

Can you backtrack and explain what exactly you're trying to do?

Ka13n

I want to do like categorized the words or some approach in order to classify the papers which I collected.
Don't know if this is possible, and thanks for helping me on my dumb question... 

sgenzer

ah ok. I'd recommend looking at this thesis that was written not long ago. I believe it is more in line of what you're trying to do.