Simplified Kernel-Based Cost-Sensitive Broad Learning System for Imbalanced Fault Diagnosis
Kaixiang Yang, Wuxing Chen, Yifan Shi, Zhiwen Yu, C. L. Philip Chen
Abstract
In the field of intelligent manufacturing, tackling the classification challenges caused by imbalanced data is crucial. Although the broad learning system (BLS) has been recognized as an effective and efficient method, its performance wanes with imbalanced datasets. Therefore, this article proposes a novel approach named simplified kernel-based cost-sensitive broad learning system (SKCSBLS) to address these issues. Based on the framework of cost-sensitive broad learning system (CSBLS) that assigns distinctive adjustment costs for individual classes, SKCSBLS emphasizes the importance of the minority class while mitigating the impact of data imbalance. Additionally, considering the complexity introduced by noisy or overlapping data points, we incorporate kernel mapping into the CSBLS. This improvement not only improves the system's capability to handle overlapping classes of samples, but also improves the overall classification effectiveness. Our experimental results highlight the potential of SKCSBLS in overcoming the challenges inherent in unbalanced data, providing a robust solution for advanced fault diagnosis in intelligent systems.