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 $

     * by a pseudo-class variable that is used by LogitBoost.
     */
    protected Instances getNumericData(Instances train) throws Exception{
  
  Instances filteredData = new Instances(train);  
  m_nominalToBinary = new NominalToBinary();      
  m_nominalToBinary.setInputFormat(filteredData);
  filteredData = Filter.useFilter(filteredData, m_nominalToBinary);  


  return super.getNumericData(filteredData);
    }

View Full Code Here


    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);

View Full Code Here

      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);

View Full Code Here

   * by a pseudo-class variable that is used by LogitBoost.
   */
  protected Instances getNumericData(Instances train) throws Exception{
  
    Instances filteredData = new Instances(train);  
    m_nominalToBinary = new NominalToBinary();      
    m_nominalToBinary.setInputFormat(filteredData);
    filteredData = Filter.useFilter(filteredData, m_nominalToBinary);  


    return super.getNumericData(filteredData);
  }

View Full Code Here

      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);

View Full Code Here


    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);

View Full Code Here

    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;

View Full Code Here

0 1

TOP

Related Classes of weka.filters.supervised.attribute.NominalToBinary

weka.classifiers.functions.LeastMedSq

weka.classifiers.functions.LinearRegression

weka.classifiers.trees.FT

weka.classifiers.trees.ft.FTtree

weka.classifiers.trees.LMT

weka.classifiers.trees.lmt.LMTNode

weka.classifiers.trees.m5.M5Base

weka.core.Attribute

weka.core.Capabilities

weka.core.DenseInstance

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.