Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods
Credit risk assessment is a crucial element in credit risk management. With the extensive
research on consumer credit risk assessment in recent decades, the abundance of literature …
research on consumer credit risk assessment in recent decades, the abundance of literature …
Research on credit rating and risk measurement of electricity retailers based on Bayesian Best Worst Method-Cloud Model and improved Credit Metrics model in …
Y Zhang, H Zhao, B Li, Y Zhao, Z Qi - Energy, 2022 - Elsevier
With the advancement of Power Market reform and the opening of the Demand Side market,
the dynamic risk management of electricity retailers has attracted more attention. This paper …
the dynamic risk management of electricity retailers has attracted more attention. This paper …
Incomplete multi-view learning: Review, analysis, and prospects
Multi-view data, stemming from diverse information sources, often suffer from
incompleteness due to various factors such as equipment failure and data transmission …
incompleteness due to various factors such as equipment failure and data transmission …
Generalized robust loss functions for machine learning
Loss function is a critical component of machine learning. Some robust loss functions are
proposed to mitigate the adverse effects caused by noise. However, they still face many …
proposed to mitigate the adverse effects caused by noise. However, they still face many …
Robust regression under the general framework of bounded loss functions
Conventional regression methods often fail when encountering noise. The application of a
bounded loss function is an effective means to enhance regressor robustness. However …
bounded loss function is an effective means to enhance regressor robustness. However …
Cost-sensitive learning with modified Stein loss function
Abstract Cost-sensitive learning (CSL), which has gained widespread attention in class
imbalance learning (CIL), can be implemented either by tuning penalty parameters or by …
imbalance learning (CIL), can be implemented either by tuning penalty parameters or by …
Multi-view cost-sensitive kernel learning for imbalanced classification problem
Multi-view imbalanced learning concentrates on recognizing valuable patterns from multi-
view imbalanced data. There are numerous algorithm-level multi-view imbalanced learning …
view imbalanced data. There are numerous algorithm-level multi-view imbalanced learning …
Enhancing enterprise credit risk assessment with cascaded multi-level graph representation learning
L Song, H Li, Y Tan, Z Li, X Shang - Neural Networks, 2024 - Elsevier
Abstract The assessment of Enterprise Credit Risk (ECR) is a critical technique for
investment decisions and financial regulation. Previous methods usually construct …
investment decisions and financial regulation. Previous methods usually construct …
Kernel methods with asymmetric and robust loss function
The least squares support vector machine (LSSVM) has achieved great success in various
fields, but it still has certain limitations. Firstly, it treats all points equally and does not take …
fields, but it still has certain limitations. Firstly, it treats all points equally and does not take …
Robust multi-view learning with the bounded LINEX loss
Multi-view learning, as a promising direction, emphasizes the consensus principle and the
complementarity principle to boost the performance. By exploiting view-consistency or view …
complementarity principle to boost the performance. By exploiting view-consistency or view …