Comparing decomposition methods for classification

Decomposition methods for multiclass classification problems constitute a powerful framework to improve generalization capabilities of a large set of learning machines, including support vector machines and multi-layer perceptrons. We present a review of the main decomposition approach to classification and an experimental comparison of One-Per-Class (OPC), Correcting Classifiers (CC) and Error Correcting Output Codes (ECOC) decomposition methods implemented using multi-layer perceptrons as dichotomizers. The results show that CC and ECOC outperform OPC over the considered data sets