One of the main factors affecting the effectiveness of ECOC methods for classification is the dependence among the errors of the computed codeword bits. We present an extended experimental work for evaluating the dependence among output errors of the decomposition unit of ECOC learning machines. In particular we quantitatively compare the dependence between ECOC Multi Layer Perceptrons (ECOC MLP), made up by a single MLP, and ECOC ensembles made up by a set of independent and parallel dichotomizers (ECOC PND), using measures based on mutual information. The experimentation analyzes the relations between the design, the dependence among output errors and the performances of ECOC learning machines. Results show that the dependence among computed codeword bits is significantly smaller for ECOC PND, pointing out that ensembles of independent dichotomizers are better suited for implementing ECOC classification methods. Moreover the experimental results suggest the research of new architectures of ECOC learning machines for improving the independence among output errors and the diversity between base learners
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