Yu Tao, Huang Wei, Tang Xin, Zheng Duosi
School of Mathematics, Harbin Institute of Technology, Harbin, China.
Department of Mathematics, Southern University of Science and Technology, Shenzhen, China.
PLoS One. 2025 Jan 10;20(1):e0316557. doi: 10.1371/journal.pone.0316557. eCollection 2025.
In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to challenges such as imbalanced data, local optima, and parameter adjustment complexities. Thus, this paper introduces a novel hybrid unsupervised classification method, named the two-stage hybrid system with spectral clustering and semi-supervised support vector machine (TSC-SVM), which effectively addresses the unsupervised imbalance problem in credit risk assessment by targeting global optimal solutions. Furthermore, a multi-view combined unsupervised method is designed to thoroughly mine data and enhance the robustness of label predictions. This method mitigates discrepancies in prediction outcomes from three distinct perspectives. The effectiveness, efficiency, and robustness of the proposed TSC-SVM model are demonstrated through various real-world applications. The proposed algorithm is anticipated to expand the customer base for financial institutions while reducing economic losses.
在信用风险评估中，可以引入无监督分类技术来降低人力资源成本并加快决策速度。尽管无监督学习方法在处理未标记数据集方面具有有效性，但其性能仍受到诸如数据不平衡、局部最优和参数调整复杂性等挑战的限制。因此，本文引入了一种新颖的混合无监督分类方法，即具有谱聚类和半监督支持向量机的两阶段混合系统（TSC-SVM），该方法通过针对全局最优解有效地解决了信用风险评估中的无监督不平衡问题。此外，还设计了一种多视图组合无监督方法，以全面挖掘数据并增强标签预测的稳健性。该方法从三个不同角度减轻了预测结果中的差异。通过各种实际应用证明了所提出的TSC-SVM模型的有效性、效率和稳健性。预计所提出的算法将扩大金融机构的客户群，同时减少经济损失。
