The imbalanced dataset’s existing classification methods have low prediction accuracy for the minority class because of the little information present. Using over- and under-sampling techniques, we can improve the minority’s ability to forecast outcomes. However, the minority class’s accuracy of prediction is negatively impacted by the two methods due to the loss of vital information or the addition of irrelevant details for classification. SVM kernels have great abilities to handle asymmetric data, but when we need to use SVM kernels alone or as part of the ensemble technique for an unbalanced dataset, we don’t have a strong reason to choose which kernel to use, and also how a particular kernel will act depends a lot on the data set. In this paper, we present a framework in which several kernel SVM (Linear, Polynomial, Sigmoid, RBF) classifiers were utilized as the base learners and one of the kernels (say RBF kernel) as meta learner using the Stacking Ensembles technique, which shows that stacked generalization of SVM kernels gives similar results as best performing kernel for an imbalanced dataset of software change proneness, using AUC, ROC, MCC, and BAS as an evaluation matrix.