Example #1
0
  /**
   * Returns description of the Decorate classifier.
   *
   * @return description of the Decorate classifier as a string
   */
  public String toString() {

    if (m_Committee == null) {
      return "Decorate: No model built yet.";
    }
    StringBuffer text = new StringBuffer();
    text.append("Decorate base classifiers: \n\n");
    for (int i = 0; i < m_Committee.size(); i++)
      text.append(((Classifier) m_Committee.get(i)).toString() + "\n\n");
    text.append("Number of classifier in the ensemble: " + m_Committee.size() + "\n");
    return text.toString();
  }
Example #2
0
  /**
   * Calculates the class membership probabilities for the given test instance.
   *
   * @param instance the instance to be classified
   * @return predicted class probability distribution
   * @exception Exception if distribution can't be computed successfully
   */
  public double[] distributionForInstance(Instance instance) throws Exception {
    if (instance.classAttribute().isNumeric()) {
      throw new UnsupportedClassTypeException("Decorate can't handle a numeric class!");
    }
    double[] sums = new double[instance.numClasses()], newProbs;
    Classifier curr;

    for (int i = 0; i < m_Committee.size(); i++) {
      curr = (Classifier) m_Committee.get(i);
      newProbs = curr.distributionForInstance(instance);
      for (int j = 0; j < newProbs.length; j++) sums[j] += newProbs[j];
    }
    if (Utils.eq(Utils.sum(sums), 0)) {
      return sums;
    } else {
      Utils.normalize(sums);
      return sums;
    }
  }
Example #3
0
  /**
   * Build Decorate classifier
   *
   * @param data the training data to be used for generating the classifier
   * @exception Exception if the classifier could not be built successfully
   */
  public void buildClassifier(Instances data) throws Exception {
    if (m_Classifier == null) {
      throw new Exception("A base classifier has not been specified!");
    }
    if (data.checkForStringAttributes()) {
      throw new UnsupportedAttributeTypeException("Cannot handle string attributes!");
    }
    if (data.classAttribute().isNumeric()) {
      throw new UnsupportedClassTypeException("Decorate can't handle a numeric class!");
    }
    if (m_NumIterations < m_DesiredSize)
      throw new Exception("Max number of iterations must be >= desired ensemble size!");

    // initialize random number generator
    if (m_Seed == -1) m_Random = new Random();
    else m_Random = new Random(m_Seed);

    int i = 1; // current committee size
    int numTrials = 1; // number of Decorate iterations
    Instances divData = new Instances(data); // local copy of data - diversity data
    divData.deleteWithMissingClass();
    Instances artData = null; // artificial data

    // compute number of artficial instances to add at each iteration
    int artSize = (int) (Math.abs(m_ArtSize) * divData.numInstances());
    if (artSize == 0) artSize = 1; // atleast add one random example
    computeStats(data); // Compute training data stats for creating artificial examples

    // initialize new committee
    m_Committee = new Vector();
    Classifier newClassifier = m_Classifier;
    newClassifier.buildClassifier(divData);
    m_Committee.add(newClassifier);
    double eComm = computeError(divData); // compute ensemble error
    if (m_Debug)
      System.out.println(
          "Initialize:\tClassifier " + i + " added to ensemble. Ensemble error = " + eComm);

    // repeat till desired committee size is reached OR the max number of iterations is exceeded
    while (i < m_DesiredSize && numTrials < m_NumIterations) {
      // Generate artificial training examples
      artData = generateArtificialData(artSize, data);

      // Label artificial examples
      labelData(artData);
      addInstances(divData, artData); // Add new artificial data

      // Build new classifier
      Classifier tmp[] = Classifier.makeCopies(m_Classifier, 1);
      newClassifier = tmp[0];
      newClassifier.buildClassifier(divData);
      // Remove all the artificial data
      removeInstances(divData, artSize);

      // Test if the new classifier should be added to the ensemble
      m_Committee.add(newClassifier); // add new classifier to current committee
      double currError = computeError(divData);
      if (currError <= eComm) { // adding the new member did not increase the error
        i++;
        eComm = currError;
        if (m_Debug)
          System.out.println(
              "Iteration: "
                  + (1 + numTrials)
                  + "\tClassifier "
                  + i
                  + " added to ensemble. Ensemble error = "
                  + eComm);
      } else { // reject the current classifier because it increased the ensemble error
        m_Committee.removeElementAt(m_Committee.size() - 1); // pop the last member
      }
      numTrials++;
    }
  }