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Fuzzy rule-based classification systems (FRBCSs) are able to design interpretable classifiers but suffer from the curse of dimensionality when dealing with complex problems with a large number of features. In this contribution we explore the use of popular approaches for designing ensembles of classifiers in the machine learning field, bagging and random subspace, to design FRBCS multiclassifiers from a basic, heuristic fuzzy classification rule generation method, aiming to both improve their accuracy and to make them able to deal with high dimensional classification problems. Besides, a multicriteria genetic algorithm is proposed to select the component classifiers in the ensemble guided by the cumulative likelihood in order to look for an appropriate accuracy-complexity trade-off.