Cascade classifiers for multiclass problems

We discuss a cascade approach to multiclass classification problems which breaks the original task into smaller subproblems in a divide-andconquer strategy. We use a splitting strategy based on the confusion matrix associated to the first (primary) classifier of the cascade, sorting test samples into independent problems according to the columns of this matrix. We test all possible combinations of three state-of-the-art classification algorithms, applying them alternatively in the two stages of the method. Performances of all these combined classifiers are evaluated on 7 real-world datasets.