Examples of weka.filters.supervised.attribute.NominalToBinary

Package weka.filters.supervised.attribute

Examples of weka.filters.supervised.attribute.NominalToBinary

weka.filters.supervised.attribute.NominalToBinary
Converts all nominal attributes into binary numeric attributes. An attribute with k values is transformed into k binary attributes if the class is nominal (using the one-attribute-per-value approach). Binary attributes are left binary, if option '-A' is not given.If the class is numeric, k - 1 new binary attributes are generated in the manner described in "Classification and Regression Trees" by Breiman et al. (i.e. taking the average class value associated with each attribute value into account)

For more information, see:

L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone (1984). Classification and Regression Trees. Wadsworth Inc.
BibTeX:
```
 @book{Breiman1984, author = {L. Breiman and J.H. Friedman and R.A. Olshen and C.J. Stone}, publisher = {Wadsworth Inc}, title = {Classification and Regression Trees}, year = {1984}, ISBN = {0412048418} } 
```
Valid options are:
```
 -N Sets if binary attributes are to be coded as nominal ones.
```
```
 -A For each nominal value a new attribute is created,  not only if there are more than 2 values.
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5541 $

      data.deleteWithMissingClass();
    }


    // Preprocess instances
    if (!m_checksTurnedOff) {
      m_TransformFilter = new NominalToBinary();
      m_TransformFilter.setInputFormat(data);
      data = Filter.useFilter(data, m_TransformFilter);
      m_MissingFilter = new ReplaceMissingValues();
      m_MissingFilter.setInputFormat(data);
      data = Filter.useFilter(data, m_MissingFilter);

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      data.deleteWithMissingClass();
    }


    // Preprocess instances
    if (!m_checksTurnedOff) {
      m_TransformFilter = new NominalToBinary();
      m_TransformFilter.setInputFormat(data);
      data = Filter.useFilter(data, m_TransformFilter);
      m_MissingFilter = new ReplaceMissingValues();
      m_MissingFilter.setInputFormat(data);
      data = Filter.useFilter(data, m_MissingFilter);

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   * @throws Exception if an error occurs
   */
  private void cleanUpData(Instances data)throws Exception{


    m_Data = data;
    m_TransformFilter = new NominalToBinary();
    m_TransformFilter.setInputFormat(m_Data);
    m_Data = Filter.useFilter(m_Data, m_TransformFilter);
    m_MissingFilter = new ReplaceMissingValues();
    m_MissingFilter.setInputFormat(m_Data);
    m_Data = Filter.useFilter(m_Data, m_MissingFilter);

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    m_replaceMissing = new ReplaceMissingValues();
    m_replaceMissing.setInputFormat(m_instances);
    m_instances = Filter.useFilter(m_instances, m_replaceMissing);


    m_nominalToBinary = new NominalToBinary();
    m_nominalToBinary.setInputFormat(m_instances);
    m_instances = Filter.useFilter(m_instances, m_nominalToBinary);


    m_removeUseless = new RemoveUseless();
    m_removeUseless.setInputFormat(m_instances);

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    m_replaceMissing.setInputFormat(filteredData);  
    filteredData = Filter.useFilter(filteredData, m_replaceMissing);  
  
    //possibly convert nominal attributes globally
    if (m_convertNominal) {      
      m_nominalToBinary = new NominalToBinary();
      m_nominalToBinary.setInputFormat(filteredData);  
      filteredData = Filter.useFilter(filteredData, m_nominalToBinary);
    }


    int minNumInstances = 2;

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    m_replaceMissing.setInputFormat(filteredData);  
    filteredData = Filter.useFilter(filteredData, m_replaceMissing);
    
    //possibly convert nominal attributes globally
    if (m_convertNominal) {      
      m_nominalToBinary = new NominalToBinary();
      m_nominalToBinary.setInputFormat(filteredData);  
      filteredData = Filter.useFilter(filteredData, m_nominalToBinary);
    }
  
    int minNumInstances = 2;

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    m_replaceMissing.setInputFormat(filteredData);  
    filteredData = Filter.useFilter(filteredData, m_replaceMissing);  
  
    //possibly convert nominal attributes globally
    if (m_convertNominal) {      
      m_nominalToBinary = new NominalToBinary();
      m_nominalToBinary.setInputFormat(filteredData);  
      filteredData = Filter.useFilter(filteredData, m_nominalToBinary);
    }


    int minNumInstances = 2;

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   * @throws Exception if an error occurs
   */
  private void cleanUpData(Instances data)throws Exception{


    m_Data = data;
    m_TransformFilter = new NominalToBinary();
    m_TransformFilter.setInputFormat(m_Data);
    m_Data = Filter.useFilter(m_Data, m_TransformFilter);
    m_MissingFilter = new ReplaceMissingValues();
    m_MissingFilter.setInputFormat(m_Data);
    m_Data = Filter.useFilter(m_Data, m_MissingFilter);

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    m_replaceMissing.setInputFormat(filteredData);  
    filteredData = Filter.useFilter(filteredData, m_replaceMissing);
    
    //possibly convert nominal attributes globally
    if (m_convertNominal) {      
      m_nominalToBinary = new NominalToBinary();
      m_nominalToBinary.setInputFormat(filteredData);  
      filteredData = Filter.useFilter(filteredData, m_nominalToBinary);
    }
  
    int minNumInstances = 2;

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     */
    protected int tryLogistic(Instances data) throws Exception{
  
  //convert nominal attributes
  Instances filteredData = new Instances(data);  
  NominalToBinary nominalToBinary = new NominalToBinary();      
  nominalToBinary.setInputFormat(filteredData);
  filteredData = Filter.useFilter(filteredData, nominalToBinary);  
  
  LogisticBase logistic = new LogisticBase(0,true,m_errorOnProbabilities);
  
  //limit LogitBoost to 200 iterations (speed)

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