Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines

Error Correcting Output Coding (ECOC) methods for multiclass classification present several open problems ranging from the trade-off between their error recovering capabilities and the learnability of the induced dichotomies to the selection of proper base learners and to the design of well-separated codes for a given multiclass problem. We experimentally analyse some of the main factors affecting the effectiveness of ECOC methods. We show that the architecture of ECOC learning machines influences the accuracy of the ECOC classifier, highlighting that ensembles of parallel and independent dichotomic Multi-Layer Perceptrons are well-suited to implement ECOC methods. We quantitatively evaluate the dependence among codeword bit errors using mutual information based measures, experimentally showing that a low dependence enhances the generalisation capabilities of ECOC. Moreover we show that the proper selection of the base learner and the decoding function of the reconstruction stage significantly affects the performance of the ECOC ensemble. The analysis of the relationships between the error recovering power, the accuracy of the base learners, and the dependence among codeword bits show that all these factors concur to the effectiveness of ECOC methods in a not straightforward way, very likely dependent on the distribution and complexity of the data.