When individual classifiers are combined appropriately, we usually obtain a better performance in terms of classification precision. Classifier ensembles are the result of combining several individual classifiers. In this work we propose and compare various consensus based combination methods to obtain the final decision of the ensemble based on fuzzy decision trees in order to improve results. We make a comparative study with several datasets to show the efficiency of the various combination methods.