Support vector machines (SVMs) are among the most popular classification algorithms. However, whereas SVMs perform efficiently in a class balanced dataset, their performance declines for class imbalanced datasets. The fuzzy SVMfor class imbalance learning (FSVM-CIL) is a variation of the SVMtype algorithm to accommodate class imbalanced datasets. Considering the class imbalance, FSVM-CIL associates a fuzzy membership to each example, which represents the importance of the example for classification. Based on FSVM-CIL, we present a simple but effective method here to calculate fuzzy memberships using the kernel mean. The kernel mean is a useful statistic for consideration of the probability distribution over the feature space. Our proposed method is simpler than preceding methods because it requires adjustment of fewer parameters and operates at reduced computational cost. Experimental results show that our proposed method is promising.