A Novel Classification Method Based on Stacking Ensemble for Imbalanced Problems
Zengshuai Wang, Minhua Zheng, Peter Liu
Abstract
Imbalanced problems are a significant challenge in data-driven diagnosis technology, as the data collected from the scenario of engineering is usually imbalanced. Imbalanced data poses a negative effect on classification algorithms and reduces the recognition performance of the minority samples. To address this challenge, ensemble classifiers have gained significant attention due to their superior performance compared to the individual classifiers. In this paper, we provide improvements at both the data and algorithm levels by using the stacking ensemble. At the data level, a Data-Space balanced Partition (DSP) method based on under-sampling 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 to 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.