Rejection rate with Adaboost.M2

[Hayfa Azibi]

Hello, 

I am working with adaboost.M2 to improve the performance of a weak classifier. My goal is to display the rejection rate at each iteration. However, my code doesn't take into account the previous weak classifiers  when classifying instances. I am using the following code:  

public double [] distributionForInstance(Instance instance) 

    throws Exception {

      

    // default model?

    if (m_CNC!= null) {

      return m_CNC.distributionForInstance(instance);

    }

    if (m_NumIterationsPerformed == 0) {

      throw new Exception("No model built");

    }

    double [] sums = new double [instance.numClasses()]; 

      for (int i = 0; i < m_NumIterationsPerformed; i++) {

      double classification = m_Classifiers[i].classifyInstance(instance);

      if (Utils.isMissingValue(classification)) {

      return sums; 

      } 

      else { 

      sums[(int)m_Classifiers[i].classifyInstance(instance)] += m_Betas[i];

      }

      }

      return Utils.logs2probs(sums);

    }

   

What should be done to resolve this issue ?

Sincerely, 

Hayfa Azibi

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