Examples of weka.classifiers.meta.ensembleSelection.ModelBag.forwardSelect()

Package weka.classifiers.meta.ensembleSelection

Class weka.classifiers.meta.ensembleSelection.ModelBag

Examples of weka.classifiers.meta.ensembleSelection.ModelBag.forwardSelect()

weka.classifiers.meta.ensembleSelection.ModelBag.forwardSelect()
Forward select one model. Will add the model which has the best effect on performance. If replacement is false, and all models are chosen, no action is taken. If a model can be added, one always is (even if it hurts performance). @param withReplacement whether a model can be added more than once. @param instances The dataset, for calculating performance. @param metric The metric to which we will optimize. See EnsembleMetricHelper @throws Exception if something goes wrong

    // assuming 1.
  }
  // Now the bag is initialized, and we're ready to hillclimb.
  for (int j = 0; j < getHillclimbIterations(); ++j) {
    if (m_algorithm == ALGORITHM_FORWARD) {
      modelBag.forwardSelect(getReplacement(), data,
    m_hillclimbMetric);
    } else if (m_algorithm == ALGORITHM_BACKWARD) {
      modelBag.backwardEliminate(data, m_hillclimbMetric);
    } else if (m_algorithm == ALGORITHM_FORWARD_BACKWARD) {
      modelBag.forwardSelectOrBackwardEliminate(

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