Imagen de Portada

Improving Predictions on Highly Unbalanced Data Using Open Source Synthetic Data Upsampling

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Krchova, Ivona, Platzer, Michael, Tiwald, Paul
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data
por: Tiwald, Paul, et al.
Publicado: (2025)

Democratizing Tabular Data Access with an Open$\unicode{x2013}$Source Synthetic$\unicode{x2013}$Data SDK
por: Krchova, Ivona, et al.
Publicado: (2025)

Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework
por: Sidorenko, Andrey, et al.
Publicado: (2025)

Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGN
por: Sidorenko, Andrey, et al.
Publicado: (2025)

SAGDA: Open-Source Synthetic Agriculture Data for Africa
por: Belgaid, Abdelghani, et al.
Publicado: (2025)

FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN
por: Maliakel, Paul Joe, et al.
Publicado: (2024)

Synthetic Data Generation with LLM for Improved Depression Prediction
por: Kang, Andrea, et al.
Publicado: (2024)

Enhancing Image Classification in Small and Unbalanced Datasets through Synthetic Data Augmentation
por: De La Fuente, Neil, et al.
Publicado: (2024)

A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models
por: Du, Junliang, et al.
Publicado: (2025)

Rethinking Data Input for Point Cloud Upsampling
por: Zhang, Tongxu
Publicado: (2024)

Sleep-Like Unsupervised Replay Improves Performance when Data are Limited or Unbalanced
por: Bazhenov, Anthony, et al.
Publicado: (2024)

Knowledge Distillation Using Frontier Open-source LLMs: Generalizability and the Role of Synthetic Data
por: Shirgaonkar, Anup, et al.
Publicado: (2024)

Generating Synthetic Electronic Health Record Data: a Methodological Scoping Review with Benchmarking on Phenotype Data and Open-Source Software
por: Chen, Xingran, et al.
Publicado: (2024)

Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya
por: Bach, Jimmy, et al.
Publicado: (2026)

AdaMuS: Adaptive Multi-view Sparsity Learning for Dimensionally Unbalanced Data
por: Xu, Cai, et al.
Publicado: (2026)

Synthetic Data from Diffusion Models Improve Drug Discovery Prediction
por: Hu, Bing, et al.
Publicado: (2024)

State Representation Learning Using an Unbalanced Atlas
por: Meng, Li, et al.
Publicado: (2023)

High-dimensional Analysis of Synthetic Data Selection
por: Rezaei, Parham, et al.
Publicado: (2025)

A Comparative Study of Open-Source Libraries for Synthetic Tabular Data Generation: SDV vs. SynthCity
por: Del Gobbo, Cristian
Publicado: (2025)

Self-Improving Diffusion Models with Synthetic Data
por: Alemohammad, Sina, et al.
Publicado: (2024)

Improved Generation of Synthetic Imaging Data Using Feature-Aligned Diffusion
por: Nair, Lakshmi
Publicado: (2024)

Synthetic Flight Data Generation Using Generative Models
por: Aly, Karim, et al.
Publicado: (2026)

Neighbor Embeddings Using Unbalanced Optimal Transport Metrics
por: Rana, Muhammad, et al.
Publicado: (2025)

Robust Prediction Model for Multidimensional and Unbalanced Datasets
por: Thakar, Pooja, et al.
Publicado: (2024)

Improving the Scaling Laws of Synthetic Data with Deliberate Practice
por: Askari-Hemmat, Reyhane, et al.
Publicado: (2025)

Improving Cardiac Risk Prediction Using Data Generation Techniques
por: Cabodevila, Alexandre, et al.
Publicado: (2025)

Characterizing LLM Inference Energy-Performance Tradeoffs across Workloads and GPU Scaling
por: Maliakel, Paul Joe, et al.
Publicado: (2025)

TAMIS: Tailored Membership Inference Attacks on Synthetic Data
por: Andrey, Paul, et al.
Publicado: (2025)

Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference
por: Amad, Harry, et al.
Publicado: (2025)

Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure
por: Tugnoli, Davide, et al.
Publicado: (2026)

The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention
por: Ding, Xingyu, et al.
Publicado: (2024)

Structured Evaluation of Synthetic Tabular Data
por: Yang, Scott Cheng-Hsin, et al.
Publicado: (2024)

Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data
por: Iofinova, Eugenia, et al.
Publicado: (2026)

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
por: Zhou, Yan, et al.
Publicado: (2025)

Label Unbalance in High-frequency Trading
por: Zhao, Zijian, et al.
Publicado: (2025)

AnyUp: Universal Feature Upsampling
por: Wimmer, Thomas, et al.
Publicado: (2025)

Can Moran Eigenvectors Improve Machine Learning of Spatial Data? Insights from Synthetic Data Validation
por: Li, Ziqi, et al.
Publicado: (2025)

Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Open Source Data from PHM Data Challenges: A Review
por: Su, Hanqi, et al.
Publicado: (2023)

Mitigating Model Drift in Developing Economies Using Synthetic Data and Outliers
por: Varshavskiy, Ilyas, et al.
Publicado: (2025)

Expert Routing with Synthetic Data for Continual Learning
por: Byun, Yewon, et al.
Publicado: (2024)