A Hybrid Approach to Coping with High Dimensionality and Class Imbalance for Software Defect Prediction

High dimensionality and class imbalance are the two main problems affecting many software defect prediction. In this paper, we propose a new technique, named SelectRUSBoost, which is a form of ensemble learning that incorporates data sampling to alleviate class imbalance and feature selection to resolve high dimensionality. To evaluate the effectiveness of the new technique, we apply it to a group of datasets in the context of software defect prediction. We employ two classification learners and six feature selection techniques. We compare the technique to the approach where feature selection and data sampling are used together, as well as the case where feature selection is used alone (no sampling used at all). The experimental results demonstrate that the SelectRUSBoost technique is more effective in improving classification performance compared to the other approaches.