Examples of 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 $
weka.filters.unsupervised.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, you might want to use the supervised version of this filter.
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.
```
```
 -R <col1,col2-col4,...> Specifies list of columns to act on. First and last are  valid indexes. (default: first-last)
```
```
 -V Invert matching sense of column indexes.
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5543 $

Examples of weka.filters.unsupervised.attribute.NominalToBinary

  m_ReplaceMissingValues = new ReplaceMissingValues();
  m_ReplaceMissingValues.setInputFormat(data);
  data = Filter.useFilter(data, m_ReplaceMissingValues);
  
  //convert nominal attributes
  m_NominalToBinary = new NominalToBinary();
  m_NominalToBinary.setInputFormat(data);
  data = Filter.useFilter(data, m_NominalToBinary);
  
  //create actual logistic model
  m_boostedModel = new LogisticBase(m_numBoostingIterations, m_useCrossValidation, m_errorOnProbabilities);

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

    }
  }
      }
      
      if (!onlyNumeric) {
  m_NominalToBinary = new NominalToBinary();
  m_NominalToBinary.setInputFormat(insts);
  insts = Filter.useFilter(insts, m_NominalToBinary);
      } 
      else {
  m_NominalToBinary = null;

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

    m_AttFilter = new RemoveUseless();
    m_AttFilter.setInputFormat(train);
    train = Filter.useFilter(train, m_AttFilter);
  
    // Transform attributes
    m_NominalToBinary = new NominalToBinary();
    m_NominalToBinary.setInputFormat(train);
    train = Filter.useFilter(train, m_NominalToBinary);
    
    // Save the structure for printing the model
    m_structure = new Instances(train, 0);

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

        }
      }
    }
    
    if (!onlyNumeric) {
      m_nominalToBinary = new NominalToBinary();
      m_nominalToBinary.setInputFormat(data);
      data = Filter.useFilter(data, m_nominalToBinary);
    }
    
    if (!m_dontNormalize && data.numInstances() > 0) {

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

    m_Missing = new ReplaceMissingValues();
    m_Missing.setInputFormat(instances);
    instances = Filter.useFilter(instances, m_Missing);
    
    if (!m_onlyNumeric) {
      m_NominalToBinary = new NominalToBinary();
      m_NominalToBinary.setInputFormat(instances);
      instances = Filter.useFilter(instances, m_NominalToBinary);
    } else {
      m_NominalToBinary = null;
    }

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

    m_Train = new Instances(insts);
    m_ReplaceMissingValues = new ReplaceMissingValues();
    m_ReplaceMissingValues.setInputFormat(m_Train);
    m_Train = Filter.useFilter(m_Train, m_ReplaceMissingValues);
    
    m_NominalToBinary = new NominalToBinary();
    m_NominalToBinary.setInputFormat(m_Train);
    m_Train = Filter.useFilter(m_Train, m_NominalToBinary);


    /** Randomize training data */
    m_Train.randomize(new Random(m_Seed));

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

    }
  }
      }
      
      if (!onlyNumeric) {
  m_NominalToBinary = new NominalToBinary();
  // exclude the bag attribute
  m_NominalToBinary.setAttributeIndices("2-last");
      }
      else {
  m_NominalToBinary = null;

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

      m_normalizeFilter = new Normalize();
      m_normalizeFilter.setInputFormat(m_trainInstances);
      m_trainInstances = Filter.useFilter(m_trainInstances, m_normalizeFilter);
    } */


    m_nominalToBinFilter = new NominalToBinary();
    m_nominalToBinFilter.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, 
                                        m_nominalToBinFilter);
    
    // delete any attributes with only one distinct value or are all missing

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

      m_normalizeFilter.setInputFormat(m_trainInstances);
      m_trainInstances = Filter.useFilter(m_trainInstances, m_normalizeFilter);
    }
    
    // convert any nominal attributes to binary numeric attributes
    m_nominalToBinaryFilter = new NominalToBinary();
    m_nominalToBinaryFilter.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, m_nominalToBinaryFilter);
    
    // delete any attributes with only one distinct value or are all missing
    for (int i = 0; i < m_trainInstances.numAttributes(); i++) {

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

      m_normalizeFilter.setInputFormat(m_trainInstances);
      m_trainInstances = Filter.useFilter(m_trainInstances, m_normalizeFilter);
    }
    
    // convert any nominal attributes to binary numeric attributes
    m_nominalToBinaryFilter = new NominalToBinary();
    m_nominalToBinaryFilter.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, m_nominalToBinaryFilter);
    
    // delete any attributes with only one distinct value or are all missing
    for (int i = 0; i < m_trainInstances.numAttributes(); i++) {

View Full Code Here

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