Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended the basic FRBCS in order to decrease the side effects of imbalanced data by employing data-mining criteria such as confidence and support. These measures are computed from information derived from data in the sub-spaces of each fuzzy rule. The experimental results show that the proposed method can improve the classification accuracy when applied on benchmark data sets.