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 …
A comprehensive survey on applications of AI technologies to failure analysis of industrial systems
Component reliability plays a pivotal role in industrial systems, which are evolving with
larger complexity and higher dimensionality of data. It is insufficient to ensure reliability and …
larger complexity and higher dimensionality of data. It is insufficient to ensure reliability and …
Adaptive KNN and graph-based auto-weighted multi-view consensus spectral learning
Z Jiang, X Liu - Information Sciences, 2022 - Elsevier
The multi-view learning is a fundamental problem in the multimedia analysis. However, most
existing multi-view learning methods need to calculate a similarity matrix for each view. This …
existing multi-view learning methods need to calculate a similarity matrix for each view. This …
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 …
Improvement of Machine Learning-Based Modelling of Container Ship's Main Particulars with Synthetic Data
One of the main problems in the application of machine learning techniques is the need for
large amounts of data necessary to obtain a well-generalizing model. This is exacerbated for …
large amounts of data necessary to obtain a well-generalizing model. This is exacerbated for …
Imbalanced binary classification under distribution uncertainty
X Ji, S Peng, S Yang - Information Sciences, 2023 - Elsevier
Imbalanced binary classification plays an important role in many applications. Some popular
classifiers, such as logistic regression (LR), usually underestimate the probability of the …
classifiers, such as logistic regression (LR), usually underestimate the probability of the …
A hierarchical attention-based feature selection and fusion method for credit risk assessment
X Liu, Y Li, C Dai, H Zhang - Future Generation Computer Systems, 2024 - Elsevier
A stable financial environment is requisite for the continuous growth of the E-business
market, emphasizing the importance of credit risk assessment. Generally, credit risk …
market, emphasizing the importance of credit risk assessment. Generally, credit risk …
AutoEIS: Automatic feature embedding, interaction and selection on default prediction
Deep models have shown the effectiveness in various areas, eg, finance, healthcare and
recommendation system. Among them, default prediction is a major application in the …
recommendation system. Among them, default prediction is a major application in the …
Causal Discovery and Deep Learning Algorithms for Detecting Geochemical Patterns Associated with Gold-Polymetallic Mineralization: A Case Study of the …
Z Luo, R Zuo - Mathematical Geosciences, 2024 - Springer
The identification of mineral deposit footprints by processing geochemical survey data
constitutes a crucial stage in mineral exploration because it provides valuable and …
constitutes a crucial stage in mineral exploration because it provides valuable and …
MVQS: Robust multi-view instance-level cost-sensitive learning method for imbalanced data classification
Multi-view imbalanced learning is to handle the datasets with multi-view representations and
imbalanced classes. Existing multi-view imbalanced learning methods can be divided into …
imbalanced classes. Existing multi-view imbalanced learning methods can be divided into …