This paper presents a new learning approach for pattern classification applications involving imbalanced data sets. In this approach, a clustering technique is employed to resample the original training set into a smaller set of representative training exemplars, represented by weighted cluster centers and their target outputs. Based on the proposed learning approach, four training algorithms are derived for feed-forward neural networks. These algorithms are implemented and tested on three benchmark data sets. Experimental results show that with the proposed learning approach, it is possible to design networks to tackle the class imbalance problem, without compromising the overall classification performance.