Dependence among codeword bits errors in ECOC learning machines: An experimental analysis

Abstract

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 extensive experimental work for evaluating the dependence among output errors of the decomposition unit of ECOC learning machines. In particular, we compare the dependence between ECOC Multi Layer Perceptrons (ECOC monolithic), 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. In this way we can analyze the relations between performances, design and dependence among output errors in ECOC learning machines. Results quantitatively 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.