Using the confusion matrix for improving ensemble classifiers

The code matrix enables to convert a multi class problem into an ensemble of binary classifiers. We suggest a new un-weighted framework for iteratively extending the code matrix which based on confusion matrix. The confusion matrix holds important information which is exploited by the suggested framework. Evaluating the confusion matrix at each iteration enables to make a decision regarding the next one against all classifier that should be added to the current code matrix. We demonstrate the benefits of the method by applying it to Error Correcting Code based ensemble and to AdbaBoost. We use Orthogonal arrays as the basic code matrix.