A Novel Classification Method Based on Stacking Ensemble for Imbalanced Problems

Imbalanced problems are a significant challenge in data-driven diagnosis technology, as the data collected from the scenario of engineering are usually imbalanced. Imbalanced data pose a negative effect on classification algorithms and reduce the recognition performance of the minority samples. To address this challenge, ensemble classifiers have gained significant attention due to their superior performance compared with the individual classifiers. In this article, we provide improvements at both the data and algorithm levels using the stacking ensemble. At the data level, a data-space balanced partition (DSP) method based on undersampling is proposed, which recursively partitions the imbalanced data space into relatively balanced data subspaces. At the algorithm level, we propose a cost-sensitive stacking approach, which adjusts the weights of different categories to enhance the algorithm’s attention to the minority class. Based on these two improvements, a novel classification method for imbalanced problems, Data-Space balanced Partition based on Cost-sensitive Stacking learning (DPCStacking), is proposed. The proposed method is evaluated on 38 imbalanced datasets from KEEL. The experimental results demonstrate the effectiveness of the proposed improvements. Compared with the other six algorithms specifically designed to address imbalanced problems, the DPCStacking algorithm achieves the highest average accuracy (AAcc) and true positive rate (TPR) of 0.876 and 0.876, respectively.