There are many areas where conventional supervised machine learning does not work well, for instance, in cases with a large, or systematically increasing, number of countably infinite classes. Zero-shot learning has been proposed to address this. In generalized settings, the zero-shot learning problem represents real-world applications where test instances are present during inference. Separately, recently, there has been increasing interest in meta-classifiers, which combine the results from individual classifications to improve the overall classification quality. In this context, the purpose of the present paper is two-fold: First, the performance of five state-of-the-art, generalized zero-shot learning methods is compared for five popular benchmark datasets. Second, six standard meta-classification approaches are tested by experiment. In the experiments undertaken, all meta-classifiers were applied to the same datasets; their performance was compared to each other and to the original classifiers.