I need help for improve my Confusion Matrix

luistops

Hi im new in Rapidminer, and i start a project to clasificate news in 2 types, 1= gender violence and 2= generic news.

This is my transforms etc that i used for :

https://gyazo.com/aae6e06bc6f1f7623881876b6ed06d23

This is my procesing text transformations:

https://gyazo.com/ed3d19158038d8c52f02db98ae13ba67

My cross validation:

https://gyazo.com/af702c35350dcd735ae61bf2f2322586

My result:

https://gyazo.com/a7752521e886c68be0490d3150b78635

 

Myquestion is how can i improve my result, and also for example if i choose a animal violence news, rapidminer fail and select a animal violence news as gender violence, iit is possible to create a rule so that in case it finds for example the word Animal, Dog, etc classify it as generic news?

Thanks for help, sorry for my english

sgenzer

hello @luistops - welcome to the Community.  We'd be happy to help you with this.  Can you please post your process XML in this thread using the </> button?  Thanks.

Scott

 

luistops

<?xml version="1.0" encoding="UTF-8"?><process version="7.6.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.6.001" 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="text:process_document_from_file" compatibility="7.5.000" expanded="true" height="82" name="Process Documents from Files" width="90" x="112" y="34">
        <list key="text_directories">
          <parameter key="Genericas" value="C:\Noticias Genéricas"/>
          <parameter key="Violencia" value="C:\Noticias de Genero"/>
        </list>
        <parameter key="file_pattern" value="*txt"/>
        <parameter key="extract_text_only" value="true"/>
        <parameter key="use_file_extension_as_type" value="true"/>
        <parameter key="content_type" value="txt"/>
        <parameter key="encoding" value="UTF-8"/>
        <parameter key="create_word_vector" value="true"/>
        <parameter key="vector_creation" value="TF-IDF"/>
        <parameter key="add_meta_information" value="true"/>
        <parameter key="keep_text" value="false"/>
        <parameter key="prune_method" value="none"/>
        <parameter key="prune_below_percent" value="3.0"/>
        <parameter key="prune_above_percent" value="30.0"/>
        <parameter key="prune_below_rank" value="0.05"/>
        <parameter key="prune_above_rank" value="0.95"/>
        <parameter key="datamanagement" value="double_sparse_array"/>
        <parameter key="data_management" value="auto"/>
        <process expanded="true">
          <operator activated="true" class="text:replace_tokens" compatibility="7.5.000" expanded="true" height="68" name="Replace Tokens" width="90" x="112" y="136">
            <list key="replace_dictionary">
              <parameter key="titleHTML;desHTML;fechaHTML" value=" "/>
              <parameter key="TituloLimpio;Descripcion;FechaF" value=" "/>
            </list>
          </operator>
          <operator activated="true" class="text:transform_cases" compatibility="7.5.000" expanded="true" height="68" name="Transform Cases" width="90" x="313" y="34">
            <parameter key="transform_to" value="lower case"/>
          </operator>
          <operator activated="true" class="text:tokenize" compatibility="7.5.000" expanded="true" height="68" name="Tokenize" width="90" x="447" y="34">
            <parameter key="mode" value="non letters"/>
            <parameter key="characters" value=".:"/>
            <parameter key="language" value="English"/>
            <parameter key="max_token_length" value="3"/>
          </operator>
          <operator activated="true" class="text:filter_stopwords_dictionary" compatibility="7.5.000" expanded="true" height="82" name="Filter Stopwords (Dictionary)" width="90" x="380" y="136">
            <parameter key="file" value="D:\stopWordsSpanish.txt"/>
            <parameter key="case_sensitive" value="false"/>
            <parameter key="encoding" value="UTF-8"/>
          </operator>
          <operator activated="true" class="text:stem_snowball" compatibility="7.5.000" expanded="true" height="68" name="Stem (Snowball)" width="90" x="380" y="238">
            <parameter key="language" value="Spanish"/>
          </operator>
          <connect from_port="document" to_op="Replace Tokens" to_port="document"/>
          <connect from_op="Replace Tokens" from_port="document" to_op="Transform Cases" to_port="document"/>
          <connect from_op="Transform Cases" from_port="document" to_op="Tokenize" to_port="document"/>
          <connect from_op="Tokenize" from_port="document" to_op="Filter Stopwords (Dictionary)" to_port="document"/>
          <connect from_op="Filter Stopwords (Dictionary)" from_port="document" to_op="Stem (Snowball)" to_port="document"/>
          <connect from_op="Stem (Snowball)" from_port="document" to_port="document 1"/>
          <portSpacing port="source_document" spacing="0"/>
          <portSpacing port="sink_document 1" spacing="0"/>
          <portSpacing port="sink_document 2" spacing="0"/>
          <description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="112" y="380">Type your comment</description>
        </process>
      </operator>
      <operator activated="true" class="concurrency:cross_validation" compatibility="7.6.001" expanded="true" height="145" name="Cross Validation" width="90" x="447" y="34">
        <parameter key="split_on_batch_attribute" value="false"/>
        <parameter key="leave_one_out" value="false"/>
        <parameter key="number_of_folds" value="9"/>
        <parameter key="sampling_type" value="automatic"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <parameter key="enable_parallel_execution" value="true"/>
        <process expanded="true">
          <operator activated="true" class="k_nn" compatibility="7.6.001" expanded="true" height="82" name="k-NN" width="90" x="179" y="34">
            <parameter key="k" value="2"/>
            <parameter key="weighted_vote" value="false"/>
            <parameter key="measure_types" value="MixedMeasures"/>
            <parameter key="mixed_measure" value="MixedEuclideanDistance"/>
            <parameter key="nominal_measure" value="NominalDistance"/>
            <parameter key="numerical_measure" value="EuclideanDistance"/>
            <parameter key="divergence" value="GeneralizedIDivergence"/>
            <parameter key="kernel_type" value="radial"/>
            <parameter key="kernel_gamma" value="1.0"/>
            <parameter key="kernel_sigma1" value="1.0"/>
            <parameter key="kernel_sigma2" value="0.0"/>
            <parameter key="kernel_sigma3" value="2.0"/>
            <parameter key="kernel_degree" value="3.0"/>
            <parameter key="kernel_shift" value="1.0"/>
            <parameter key="kernel_a" value="1.0"/>
            <parameter key="kernel_b" value="0.0"/>
          </operator>
          <connect from_port="training set" to_op="k-NN" to_port="training set"/>
          <connect from_op="k-NN" from_port="model" to_port="model"/>
          <portSpacing port="source_training set" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
          <description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="11" y="419">Diccionario de sinonimos&lt;br/&gt;Si aparece la palabra perro no es de violencia de genero=violencia animal&lt;br/&gt;</description>
          <description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="137" y="403">Type your comment</description>
        </process>
        <process expanded="true">
          <operator activated="true" class="apply_model" compatibility="7.6.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
            <list key="application_parameters"/>
            <parameter key="create_view" value="false"/>
          </operator>
          <operator activated="true" class="performance_binominal_classification" compatibility="7.6.001" expanded="true" height="82" name="Performance (2)" width="90" x="179" y="187">
            <parameter key="main_criterion" value="accuracy"/>
            <parameter key="accuracy" value="true"/>
            <parameter key="classification_error" value="true"/>
            <parameter key="kappa" value="true"/>
            <parameter key="AUC (optimistic)" value="true"/>
            <parameter key="AUC" value="true"/>
            <parameter key="AUC (pessimistic)" value="true"/>
            <parameter key="precision" value="true"/>
            <parameter key="recall" value="true"/>
            <parameter key="lift" value="true"/>
            <parameter key="fallout" value="true"/>
            <parameter key="f_measure" value="true"/>
            <parameter key="false_positive" value="true"/>
            <parameter key="false_negative" value="true"/>
            <parameter key="true_positive" value="true"/>
            <parameter key="true_negative" value="true"/>
            <parameter key="sensitivity" value="true"/>
            <parameter key="specificity" value="true"/>
            <parameter key="youden" value="true"/>
            <parameter key="positive_predictive_value" value="true"/>
            <parameter key="negative_predictive_value" value="true"/>
            <parameter key="psep" value="true"/>
            <parameter key="skip_undefined_labels" value="true"/>
            <parameter key="use_example_weights" value="true"/>
          </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 (2)" to_port="labelled data"/>
          <connect from_op="Performance (2)" from_port="performance" to_port="performance 1"/>
          <portSpacing port="source_model" spacing="0"/>
          <portSpacing port="source_test set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="sink_test set results" spacing="0"/>
          <portSpacing port="sink_performance 1" spacing="0"/>
          <portSpacing port="sink_performance 2" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Process Documents from Files" from_port="example set" to_op="Cross Validation" to_port="example set"/>
      <connect from_op="Cross Validation" from_port="example set" to_port="result 1"/>
      <connect from_op="Cross Validation" from_port="performance 1" to_port="result 2"/>
      <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"/>
    </process>
  </operator>
</process>

sgenzer

hello @luistops - ok thanks for that.  So for starters to improve your model I would encourage you to optimize your parameters (see https://www.youtube.com/watch?v=nXjjAA2mDMY).  As for reducing/combining classes, I'd need to see your dataset to understand better what you're trying to do.

 

Scott

luistops

Ok thank you so much for help, i include my datashet. my proyect just try to diference and clasificate news in 2 categories:

1. No gender violence

2. Gender violence

sgenzer

hello @luistops - ok I think I get your idea.  I did not have your Spanish stopword dictionary so I just greyed that out.  As for animal, dog, etc.. I put some replace token lines in your Process Documents operator that perhaps (?) is what you're looking for.  Note that the "test set" I created is rather small so don't get too excited by your 100% accuracy result. 

 

<?xml version="1.0" encoding="UTF-8"?><process version="7.6.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.6.001" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="text:process_document_from_file" compatibility="7.5.000" expanded="true" height="82" name="Process Documents from Files" width="90" x="112" y="34">
        <list key="text_directories">
          <parameter key="Genericas" value="/Users/GenzerConsulting/OneDrive - RapidMiner/OneDrive Repository/random community stuff/newsfromhtml1/No gender violence news"/>
          <parameter key="Violencia" value="/Users/GenzerConsulting/OneDrive - RapidMiner/OneDrive Repository/random community stuff/newsfromhtml1/Gender violence news"/>
        </list>
        <parameter key="file_pattern" value="*txt"/>
        <parameter key="encoding" value="UTF-8"/>
        <process expanded="true">
          <operator activated="true" class="text:replace_tokens" compatibility="7.5.000" expanded="true" height="68" name="Replace Tokens" width="90" x="45" y="34">
            <list key="replace_dictionary">
              <parameter key="titleHTML;desHTML;fechaHTML" value=" "/>
              <parameter key="TituloLimpio;Descripcion;FechaF" value=" "/>
              <parameter key="animal.*violence" value="generic news"/>
              <parameter key="dog.*violence" value="generic news"/>
            </list>
          </operator>
          <operator activated="true" class="text:transform_cases" compatibility="7.5.000" expanded="true" height="68" name="Transform Cases" width="90" x="179" y="34"/>
          <operator activated="true" class="text:tokenize" compatibility="7.5.000" expanded="true" height="68" name="Tokenize" width="90" x="313" y="34"/>
          <operator activated="false" class="text:filter_stopwords_dictionary" compatibility="7.5.000" expanded="true" height="82" name="Filter Stopwords (Dictionary)" width="90" x="380" y="136">
            <parameter key="file" value="D:\stopWordsSpanish.txt"/>
            <parameter key="encoding" value="UTF-8"/>
          </operator>
          <operator activated="true" class="text:stem_snowball" compatibility="7.5.000" expanded="true" height="68" name="Stem (Snowball)" width="90" x="447" y="34">
            <parameter key="language" value="Spanish"/>
          </operator>
          <connect from_port="document" to_op="Replace Tokens" to_port="document"/>
          <connect from_op="Transform Cases" from_port="document" to_op="Tokenize" to_port="document"/>
          <connect from_op="Tokenize" from_port="document" to_op="Stem (Snowball)" to_port="document"/>
          <connect from_op="Stem (Snowball)" from_port="document" to_port="document 1"/>
          <portSpacing port="source_document" spacing="0"/>
          <portSpacing port="sink_document 1" spacing="0"/>
          <portSpacing port="sink_document 2" spacing="0"/>
          <description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="112" y="380">Type your comment</description>
        </process>
      </operator>
      <operator activated="true" class="split_data" compatibility="7.6.001" expanded="true" height="103" name="Split Data" width="90" x="246" y="289">
        <enumeration key="partitions">
          <parameter key="ratio" value="0.9"/>
          <parameter key="ratio" value="0.1"/>
        </enumeration>
      </operator>
      <operator activated="true" class="optimize_parameters_grid" compatibility="7.6.001" expanded="true" height="145" name="Optimize Parameters (Grid)" width="90" x="514" y="34">
        <list key="parameters">
          <parameter key="k-NN.k" value="[2;10;10;linear]"/>
        </list>
        <process expanded="true">
          <operator activated="true" class="concurrency:cross_validation" compatibility="7.6.001" expanded="true" height="145" name="Cross Validation" width="90" x="45" y="34">
            <parameter key="number_of_folds" value="9"/>
            <process expanded="true">
              <operator activated="true" class="k_nn" compatibility="7.6.001" expanded="true" height="82" name="k-NN" width="90" x="112" y="34">
                <parameter key="k" value="10"/>
              </operator>
              <connect from_port="training set" to_op="k-NN" to_port="training set"/>
              <connect from_op="k-NN" from_port="model" to_port="model"/>
              <portSpacing port="source_training set" spacing="0"/>
              <portSpacing port="sink_model" spacing="0"/>
              <portSpacing port="sink_through 1" spacing="0"/>
              <description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="11" y="419">Diccionario de sinonimos&lt;br/&gt;Si aparece la palabra perro no es de violencia de genero=violencia animal&lt;br/&gt;</description>
              <description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="137" y="403">Type your comment</description>
            </process>
            <process expanded="true">
              <operator activated="true" class="apply_model" compatibility="7.6.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance_binominal_classification" compatibility="7.6.001" expanded="true" height="82" name="Performance (train)" width="90" x="179" y="34">
                <parameter key="main_criterion" value="accuracy"/>
                <parameter key="classification_error" value="true"/>
                <parameter key="kappa" value="true"/>
                <parameter key="AUC (optimistic)" value="true"/>
                <parameter key="AUC" value="true"/>
                <parameter key="AUC (pessimistic)" value="true"/>
                <parameter key="precision" value="true"/>
                <parameter key="recall" value="true"/>
                <parameter key="lift" value="true"/>
                <parameter key="fallout" value="true"/>
                <parameter key="f_measure" value="true"/>
                <parameter key="false_positive" value="true"/>
                <parameter key="false_negative" value="true"/>
                <parameter key="true_positive" value="true"/>
                <parameter key="true_negative" value="true"/>
                <parameter key="sensitivity" value="true"/>
                <parameter key="specificity" value="true"/>
                <parameter key="youden" value="true"/>
                <parameter key="positive_predictive_value" value="true"/>
                <parameter key="negative_predictive_value" value="true"/>
                <parameter key="psep" value="true"/>
              </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 (train)" to_port="labelled data"/>
              <connect from_op="Performance (train)" from_port="performance" to_port="performance 1"/>
              <portSpacing port="source_model" spacing="0"/>
              <portSpacing port="source_test set" spacing="0"/>
              <portSpacing port="source_through 1" spacing="0"/>
              <portSpacing port="sink_test set results" spacing="0"/>
              <portSpacing port="sink_performance 1" spacing="0"/>
              <portSpacing port="sink_performance 2" spacing="0"/>
            </process>
          </operator>
          <connect from_port="input 1" to_op="Cross Validation" to_port="example set"/>
          <connect from_op="Cross Validation" from_port="model" to_port="result 1"/>
          <connect from_op="Cross Validation" from_port="example set" to_port="result 2"/>
          <connect from_op="Cross Validation" from_port="performance 1" to_port="performance"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="source_input 2" spacing="0"/>
          <portSpacing port="sink_performance" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
          <portSpacing port="sink_result 2" spacing="0"/>
          <portSpacing port="sink_result 3" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="apply_model" compatibility="7.6.001" expanded="true" height="82" name="Apply Model (2)" width="90" x="715" y="289">
        <list key="application_parameters"/>
      </operator>
      <operator activated="true" class="performance_classification" compatibility="7.6.001" expanded="true" height="82" name="Performance (test)" width="90" x="916" y="289">
        <list key="class_weights"/>
      </operator>
      <connect from_op="Process Documents from Files" from_port="example set" to_op="Split Data" to_port="example set"/>
      <connect from_op="Split Data" from_port="partition 1" to_op="Optimize Parameters (Grid)" to_port="input 1"/>
      <connect from_op="Split Data" from_port="partition 2" to_op="Apply Model (2)" to_port="unlabelled data"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="performance" to_port="result 2"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="parameter" to_port="result 3"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="result 1" to_op="Apply Model (2)" to_port="model"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="result 2" to_port="result 1"/>
      <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance (test)" to_port="labelled data"/>
      <connect from_op="Performance (test)" from_port="performance" to_port="result 4"/>
      <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"/>
      <portSpacing port="sink_result 5" spacing="0"/>
    </process>
  </operator>
</process>

Scott

Thomas_Ott

Just looking through your initial confusion matrix you had some decent results off the bat. What k value were you using? 

 

In conjuction with what @sgenzer said, optimization is a must step. I would also look at using the Deep Learning algo as well as a LinearSVM too. 

luistops

Thank you for the help i used k=2, now i have one doubt i have 2 result, but i dont understand where its come from, i have this result https://gyazo.com/00d0fc3aa5ff57cc7eb20b8d6bc45bca thats its great.

And i have this one https://gyazo.com/79891aae0d4958d6d9b790d7bc934557

Whats is the diferent of this result because i think they use the same algorithm to calculate the result

Again thanks

sgenzer

So there's another tab in the Results view that you need to look at: ParameterSet (Optimize Parameters (Grid)).  This will tell you the value of k (assuming you used my process) that was optimal.

 

Scott