Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data
Fuente:
arXiv
Guardado en:
| Autores principales: | Sun, Xu, Qin, Zixuan, Zhang, Shun, Wang, Yuexian, Huang, Li |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision
por: Jiang, Mohan, et al.
Publicado: (2024)
por: Jiang, Mohan, et al.
Publicado: (2024)
GENIU: A Restricted Data Access Unlearning for Imbalanced Data
por: Zhang, Chenhao, et al.
Publicado: (2024)
por: Zhang, Chenhao, et al.
Publicado: (2024)
Overcoming Imbalanced Safety Data Using Extended Accident Triangle
por: Sun, Kailai, et al.
Publicado: (2024)
por: Sun, Kailai, et al.
Publicado: (2024)
A Comprehensive Survey on Imbalanced Data Learning
por: Gao, Xinyi, et al.
Publicado: (2025)
por: Gao, Xinyi, et al.
Publicado: (2025)
Data-Efficient and Robust Trajectory Generation through Pathlet Dictionary Learning
por: Tang, Yuanbo, et al.
Publicado: (2025)
por: Tang, Yuanbo, et al.
Publicado: (2025)
Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance
por: Nakada, Ryumei, et al.
Publicado: (2024)
por: Nakada, Ryumei, et al.
Publicado: (2024)
Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data
por: Qi, Yijiashun, et al.
Publicado: (2025)
por: Qi, Yijiashun, et al.
Publicado: (2025)
Crash Severity Risk Modeling Strategies under Data Imbalance
por: Mamun, Abdullah Al, et al.
Publicado: (2024)
por: Mamun, Abdullah Al, et al.
Publicado: (2024)
The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting
por: Zhang, Shuyao, et al.
Publicado: (2024)
por: Zhang, Shuyao, et al.
Publicado: (2024)
Bias-Corrected Data Synthesis for Imbalanced Learning
por: Lyu, Pengfei, et al.
Publicado: (2025)
por: Lyu, Pengfei, et al.
Publicado: (2025)
Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering
por: Zhang, Yiqun, et al.
Publicado: (2024)
por: Zhang, Yiqun, et al.
Publicado: (2024)
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
por: Kaushik, Chiraag, et al.
Publicado: (2024)
por: Kaushik, Chiraag, et al.
Publicado: (2024)
Measuring Privacy Risks and Tradeoffs in Financial Synthetic Data Generation
por: Zuo, Michael, et al.
Publicado: (2026)
por: Zuo, Michael, et al.
Publicado: (2026)
Enhancing Imbalanced Electrocardiogram Classification: A Novel Approach Integrating Data Augmentation through Wavelet Transform and Interclass Fusion
por: Shao, Haijian, et al.
Publicado: (2026)
por: Shao, Haijian, et al.
Publicado: (2026)
Analysis of Financial Risk Behavior Prediction Using Deep Learning and Big Data Algorithms
por: Yang, Haowei, et al.
Publicado: (2024)
por: Yang, Haowei, et al.
Publicado: (2024)
Bayesian Robust Financial Trading with Adversarial Synthetic Market Data
por: Xia, Haochong, et al.
Publicado: (2026)
por: Xia, Haochong, et al.
Publicado: (2026)
Synthetic Information towards Maximum Posterior Ratio for deep learning on Imbalanced Data
por: Nguyen, Hung, et al.
Publicado: (2024)
por: Nguyen, Hung, et al.
Publicado: (2024)
Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity
por: Zhang, Zixuan, et al.
Publicado: (2026)
por: Zhang, Zixuan, et al.
Publicado: (2026)
Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learning
por: Zhou, Han, et al.
Publicado: (2025)
por: Zhou, Han, et al.
Publicado: (2025)
IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification
por: Li, Zhixun, et al.
Publicado: (2025)
por: Li, Zhixun, et al.
Publicado: (2025)
Learning Confidence Bounds for Classification with Imbalanced Data
por: Clifford, Matt, et al.
Publicado: (2024)
por: Clifford, Matt, et al.
Publicado: (2024)
Data Augmentation with Variational Autoencoder for Imbalanced Dataset
por: Stocksieker, Samuel, et al.
Publicado: (2024)
por: Stocksieker, Samuel, et al.
Publicado: (2024)
Assisted Learning for Organizations with Limited Imbalanced Data
por: Chen, Cheng, et al.
Publicado: (2021)
por: Chen, Cheng, et al.
Publicado: (2021)
Histogram Approaches for Imbalanced Data Streams Regression
por: Aminian, Ehsan, et al.
Publicado: (2025)
por: Aminian, Ehsan, et al.
Publicado: (2025)
UBMF: Uncertainty-Aware Bayesian Meta-Learning Framework for Fault Diagnosis with Imbalanced Industrial Data
por: Lian, Zhixuan, et al.
Publicado: (2025)
por: Lian, Zhixuan, et al.
Publicado: (2025)
Towards Better Performance in Incomplete LDL: Addressing Data Imbalance
por: Kou, Zhiqiang, et al.
Publicado: (2024)
por: Kou, Zhiqiang, et al.
Publicado: (2024)
Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting
por: Tsoumplekas, Georgios, et al.
Publicado: (2024)
por: Tsoumplekas, Georgios, et al.
Publicado: (2024)
Kernel-Based Enhanced Oversampling Method for Imbalanced Classification
por: Li, Wenjie, et al.
Publicado: (2025)
por: Li, Wenjie, et al.
Publicado: (2025)
Enhancing Machine Learning Performance through Intelligent Data Quality Assessment: An Unsupervised Data-centric Framework
por: Rahal, Manal, et al.
Publicado: (2025)
por: Rahal, Manal, et al.
Publicado: (2025)
Data Augmentation for Classification of Negative Pregnancy Outcomes in Imbalanced Data
por: Biswas, Md Badsha
Publicado: (2025)
por: Biswas, Md Badsha
Publicado: (2025)
Imbalanced Data Clustering using Equilibrium K-Means
por: He, Yudong
Publicado: (2024)
por: He, Yudong
Publicado: (2024)
Time Series Data Augmentation as an Imbalanced Learning Problem
por: Cerqueira, Vitor, et al.
Publicado: (2024)
por: Cerqueira, Vitor, et al.
Publicado: (2024)
Improving Online Bagging for Complex Imbalanced Data Stream
por: Przybyl, Bartosz, et al.
Publicado: (2024)
por: Przybyl, Bartosz, et al.
Publicado: (2024)
Sampling Imbalanced Data with Multi-objective Bilevel Optimization
por: Medlin, Karen, et al.
Publicado: (2025)
por: Medlin, Karen, et al.
Publicado: (2025)
Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection
por: Liu, Huafeng, et al.
Publicado: (2024)
por: Liu, Huafeng, et al.
Publicado: (2024)
InCo-DPO: Balancing Distribution Shift and Data Quality for Enhanced Preference Optimization
por: Wang, Yunan, et al.
Publicado: (2025)
por: Wang, Yunan, et al.
Publicado: (2025)
Theoretical Analysis of Contrastive Learning under Imbalanced Data: From Training Dynamics to a Pruning Solution
por: Liao, Haixu, et al.
Publicado: (2026)
por: Liao, Haixu, et al.
Publicado: (2026)
Mitigating Modality Quantity and Quality Imbalance in Multimodal Online Federated Learning
por: Wang, Heqiang, et al.
Publicado: (2025)
por: Wang, Heqiang, et al.
Publicado: (2025)
Enhancing IoT Cyber Attack Detection in the Presence of Highly Imbalanced Data
por: Haque, Md. Ehsanul, et al.
Publicado: (2025)
por: Haque, Md. Ehsanul, et al.
Publicado: (2025)
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
por: Bai, Sikai, et al.
Publicado: (2023)
por: Bai, Sikai, et al.
Publicado: (2023)
Ejemplares similares
-
Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision
por: Jiang, Mohan, et al.
Publicado: (2024) -
GENIU: A Restricted Data Access Unlearning for Imbalanced Data
por: Zhang, Chenhao, et al.
Publicado: (2024) -
Overcoming Imbalanced Safety Data Using Extended Accident Triangle
por: Sun, Kailai, et al.
Publicado: (2024) -
A Comprehensive Survey on Imbalanced Data Learning
por: Gao, Xinyi, et al.
Publicado: (2025) -
Data-Efficient and Robust Trajectory Generation through Pathlet Dictionary Learning
por: Tang, Yuanbo, et al.
Publicado: (2025)