Using diversity measures for generating error-correcting output codes in classifier ensembles

摘要

Error-correcting output codes (ECOC) are used to design diverse classifier ensembles. Diversity within ECOC is traditionally measured by Hamming distance. Here we argue that this measure is insufficient for assessing the quality of code for the purposes of building accurate ensembles. We propose to use diversity measures from the literature on classifier ensembles and suggest an evolutionary algorithm to construct the code.