On the Independence of the Individual Predictions in Parallel Randomized Ensembles

In randomized parallel ensembles the class label predictions for a particular instance by different ensemble classifiers are independent random variables. Taking advantage of this independence we design a statistical test to identify instances near the decision borders, which are difficult to classify because of their proximity to these borders. For these instances, the performance of the ensemble is poor and approaches random guessing. The validity of this analysis and the usefulness of the proposed statistical test are illustrated in several real-world classification problems.