List of usage examples for weka.attributeSelection SVMAttributeEval SVMAttributeEval
public SVMAttributeEval()
From source file:ca.uottawa.balie.WekaAttributeSelection.java
License:Open Source License
/** * Select the top attributes/* w w w . j a va2 s .c o m*/ */ public void Select(boolean pi_Debug) { Instances insts = m_DummyLearner.GetTrainInstances(); try { ASEvaluation eval = null; ASSearch search = null; if (m_Evaluator == WEKA_CHI_SQUARE) { eval = new ChiSquaredAttributeEval(); search = new Ranker(); ((Ranker) search).setNumToSelect(m_NumAttributes); } else if (m_Evaluator == WEKA_INFO_GAIN) { eval = new InfoGainAttributeEval(); search = new Ranker(); ((Ranker) search).setNumToSelect(m_NumAttributes); } else if (m_Evaluator == WEKA_WRAPPER) { eval = new ClassifierSubsetEval(); ((ClassifierSubsetEval) eval).setClassifier(new NaiveBayes()); search = new Ranker(); // TODO: use something else than ranker ((Ranker) search).setNumToSelect(m_NumAttributes); } else if (m_Evaluator == WEKA_SYM_UNCERT) { eval = new SymmetricalUncertAttributeEval(); search = new Ranker(); ((Ranker) search).setNumToSelect(m_NumAttributes); } else if (m_Evaluator == WEKA_SVM) { eval = new SVMAttributeEval(); search = new Ranker(); ((Ranker) search).setNumToSelect(m_NumAttributes); } else if (m_Evaluator == WEKA_RELIEF) { eval = new ReliefFAttributeEval(); search = new Ranker(); ((Ranker) search).setNumToSelect(m_NumAttributes); } else if (m_Evaluator == WEKA_ONER) { eval = new OneRAttributeEval(); search = new Ranker(); ((Ranker) search).setNumToSelect(m_NumAttributes); } m_AttributeSelection = new AttributeSelection(); m_AttributeSelection.setEvaluator(eval); m_AttributeSelection.setSearch(search); m_AttributeSelection.SelectAttributes(insts); if (pi_Debug) System.out.println(m_AttributeSelection.toResultsString()); } catch (Exception e) { System.err.println(e.getMessage()); } }