Examples of weka.attributeSelection.Ranker

weka.attributeSelection.Ranker

Ranker :

Ranks attributes by their individual evaluations. Use in conjunction with attribute evaluators (ReliefF, GainRatio, Entropy etc).

Valid options are:

 -P <start set> Specify a starting set of attributes. Eg. 1,3,5-7. Any starting attributes specified are ignored during the ranking.

 -T <threshold> Specify a theshold by which attributes may be discarded from the ranking.

 -N <num to select> Specify number of attributes to select

@author Mark Hall (mhall@cs.waikato.ac.nz) @version $Revision: 1.26 $

      }
      setEvaluator(ASEvaluation.forName(evalClassName, evalOptions));
    }


    if (m_ASEvaluator instanceof AttributeEvaluator) {
      setSearch(new Ranker());
    }


    optionString = Utils.getOption('S',options);
    if (optionString.length() != 0) {
      optionString = optionString.trim();

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              JOptionPane.showConfirmDialog(null, Messages.getInstance().getString("AttributeSelectionPanel_Result_JOptionPaneShowConfirmDialog_Text_First")
                                            +m_AttributeEvaluatorEditor.getValue().getClass().getName()
                                            +Messages.getInstance().getString("AttributeSelectionPanel_Result_JOptionPaneShowConfirmDialog_Text_Second"),
                                            Messages.getInstance().getString("AttributeSelectionPanel_Result_JOptionPaneShowConfirmDialog_Text_Third"), JOptionPane.YES_NO_OPTION);
            if (result == JOptionPane.YES_OPTION) {
              m_AttributeSearchEditor.setValue(new Ranker());
            } else {
              // restore to what was there previously (if possible)
              if (backup != null) {
                m_AttributeEvaluatorEditor.setValue(backup);
              }

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                                            +"in order to use\n"
                                            +m_AttributeEvaluatorEditor.getValue().getClass().getName()
                                            +".\nShould I select the Ranker search method for you?",
                                            "Alert!", JOptionPane.YES_NO_OPTION);
            if (result == JOptionPane.YES_OPTION) {
              m_AttributeSearchEditor.setValue(new Ranker());
            } else {
              // restore to what was there previously (if possible)
              if (backup != null) {
                m_AttributeEvaluatorEditor.setValue(backup);
              }

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      }
      setEvaluator(ASEvaluation.forName(evalClassName, evalOptions));
    }


    if (m_ASEvaluator instanceof AttributeEvaluator) {
      setSearch(new Ranker());
    }


    optionString = Utils.getOption('S',options);
    if (optionString.length() != 0) {
      optionString = optionString.trim();

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                                            +"in order to use\n"
                                            +m_AttributeEvaluatorEditor.getValue().getClass().getName()
                                            +".\nShould I select the Ranker search method for you?",
                                            "Alert!", JOptionPane.YES_NO_OPTION);
            if (result == JOptionPane.YES_OPTION) {
              m_AttributeSearchEditor.setValue(new Ranker());
            } else {
              // restore to what was there previously (if possible)
              if (backup != null) {
                m_AttributeEvaluatorEditor.setValue(backup);
              }

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      }
      setEvaluator(ASEvaluation.forName(evalClassName, evalOptions));
    }


    if (m_ASEvaluator instanceof AttributeEvaluator) {
      setSearch(new Ranker());
    }


    optionString = Utils.getOption('S',options);
    if (optionString.length() != 0) {
      optionString = optionString.trim();

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        Random rndd = new Random(422);


        AttributeSelection attribSel = new AttributeSelection();


        att_ev_SingleBayesROCAttributeEval roc = new att_ev_SingleBayesROCAttributeEval();
        Ranker rnkr = new Ranker();


//        roc.buildEvaluator(learn);


        attribSel.setEvaluator(roc);
        attribSel.setSearch(rnkr);

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        Random rndd = new Random(422);


        AttributeSelection attribSel = new AttributeSelection();


        att_ev_TTestAttributeEval pval = new att_ev_TTestAttributeEval();
        Ranker rnkr = new Ranker();
//        rnkr.setThreshold(p_val_threshold);


//        roc.buildEvaluator(learn);


        attribSel.setEvaluator(pval);

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        //GreedyStepwise search = new GreedyStepwise();
        //search.setNumToSelect(980);
        //search.setSearchBackwards(true);
        
        Ranker search = new Ranker();
        search.setNumToSelect(980);
        
        filter.setEvaluator(eval);
        filter.setSearch(search);
        
        filter.setInputFormat(examples.getInstances());

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Related Classes of weka.attributeSelection.Ranker

bnGUI.att_BayesAttSel

bnGUI.att_TTestAttSel

etc.aloe.oilspill2010.FeatureGenerationImpl

weka.core.Option

weka.core.Range

weka.filters.supervised.attribute.AttributeSelection

weka.gui.explorer.AttributeSelectionPanel

java.util.Vector

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