Learning classifiers for imbalanced data sets is a difficult task for current machine learning algorithms. The difficulty can be traced to the fact that being accuracy driven, most algorithms lead to classifiers which are biased towards the majority class. Introducing in the learning algorithm misclassification costs, which differentiate between classes, has gone a long way towards improving the performance of the resulting classifiers. Alternatively, experiments have shown that a particular type of fuzzy classifiers apply better for imbalanced data sets. This paper explores the hypothesis that fuzzy classifiers can account to a certain extent for the error costs associated with other learning algorithms.