Examples of weka.attributeSelection.CfsSubsetEval

weka.attributeSelection.CfsSubsetEval
CfsSubsetEval :

Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them.

Subsets of features that are highly correlated with the class while having low intercorrelation are preferred.

For more information see:

M. A. Hall (1998). Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand.
BibTeX:
```
 @phdthesis{Hall1998, address = {Hamilton, New Zealand}, author = {M. A. Hall}, school = {University of Waikato}, title = {Correlation-based Feature Subset Selection for Machine Learning}, year = {1998} } 
```
Valid options are:
```
 -M Treat missing values as a separate value.
```
```
 -L Don't include locally predictive attributes.
```
@author Mark Hall (mhall@cs.waikato.ac.nz) @version $Revision: 6133 $ @see Discretize

   * set options to their default values
   */
  protected void resetOptions() {


    m_trainSelector = new weka.attributeSelection.AttributeSelection();
    setEvaluator(new CfsSubsetEval());
    setSearch(new BestFirst());
    m_SelectedAttributes = null;
    m_FilterOptions = null;
  }

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   * set options to their default values
   */
  protected void resetOptions() {


    m_trainSelector = new weka.attributeSelection.AttributeSelection();
    setEvaluator(new CfsSubsetEval());
    setSearch(new BestFirst());
    m_SelectedAttributes = null;
    m_FilterOptions = null;
  }

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   * set options to their default values
   */
  protected void resetOptions() {


    m_trainSelector = new weka.attributeSelection.AttributeSelection();
    setEvaluator(new CfsSubsetEval());
    setSearch(new BestFirst());
    m_SelectedAttributes = null;
    m_FilterOptions = null;
  }

View Full Code Here


    public void addAttSelFilter(gui_WekaExperiment de) {
        try {


            AttributeSelection attSel = new AttributeSelection();
            CfsSubsetEval eval = new CfsSubsetEval();
            GreedyStepwise search = new GreedyStepwise();
            attSel.setEvaluator(eval);
            attSel.setSearch(search);
            //weka.filters.supervised.attribute.AttributeSelection -E "weka.attributeSelection.CfsSubsetEval " -S "weka.attributeSelection.BestFirst -D 1 -N 5"
            //String[] opt = {"-E", "\"weka.attributeSelection.CfsSubsetEval \"","-s","\"weka.attributeSelection.BestFirst -D 1 -N 5\"" };

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