Cover Image

Reducing Class Bias In Data-Balanced Datasets Through Hardness-Based Resampling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pukowski, Pawel, Osmani, Venet
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data Imbalance
by: Pukowski, Pawel, et al.
Published: (2024)

Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review
by: Nafis, Nazia, et al.
Published: (2025)

Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods
by: Yousefimehr, Behnam, et al.
Published: (2025)

The Limits of Inference Scaling Through Resampling
by: Stroebl, Benedikt, et al.
Published: (2024)

General Machine Learning: Theory for Learning Under Variable Regimes
by: Osmani, Aomar
Published: (2026)

SMGI: A Structural Theory of General Artificial Intelligence
by: Osmani, Aomar
Published: (2026)

Data-Driven Policy Mapping for Safe RL-based Energy Management Systems
by: Zangato, Theo, et al.
Published: (2025)

Improving Insurance Catastrophic Data with Resampling and GAN Methods
by: Dzadz, Norbert, et al.
Published: (2024)

Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data
by: Genans, Ferdinand, et al.
Published: (2026)

Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions
by: Luo, Jiaqi, et al.
Published: (2024)

On the Necessity of Metalearning: Learning Suitable Parameterizations for Learning Processes
by: Hamidi, Massinissa, et al.
Published: (2023)

Interleaved Resampling and Refitting: Data and Compute-Efficient Evaluation of Black-Box Predictors
by: Hu, Haichen, et al.
Published: (2026)

A Resampling-Based Framework for Network Structure Learning in High-Dimensional Data
by: Huang, Ziwei, et al.
Published: (2026)

Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
by: Kaushik, Chiraag, et al.
Published: (2024)

Meta-RL with Shared Representations Enables Fast Adaptation in Energy Systems
by: Zangato, Théo, et al.
Published: (2026)

CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?
by: Alabdulmohsin, Ibrahim, et al.
Published: (2024)

Class-wise Balancing Data Replay for Federated Class-Incremental Learning
by: Qi, Zhuang, et al.
Published: (2025)

Resampling and averaging coordinates on data
by: Blumberg, Andrew J., et al.
Published: (2024)

Variance Reduction via Resampling and Experience Replay
by: Han, Jiale, et al.
Published: (2025)

Reachability Weighted Offline Goal-conditioned Resampling
by: Yang, Wenyan, et al.
Published: (2025)

Interpolating Discrete Diffusion Models with Controllable Resampling
by: Kollovieh, Marcel, et al.
Published: (2026)

A Dual Pipeline Machine Learning Framework for Automated Multi Class Sleep Disorder Screening Using Hybrid Resampling and Ensemble Learning
by: Ovi, Md Sultanul Islam, et al.
Published: (2026)

Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom
by: Djagba, Prudence, et al.
Published: (2026)

Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling
by: Wei, Xiaoli, et al.
Published: (2023)

Ctrl-Z: Controlling AI Agents via Resampling
by: Bhatt, Aryan, et al.
Published: (2025)

Decoupled Prioritized Resampling for Offline RL
by: Yue, Yang, et al.
Published: (2023)

Resampling-free Particle Filters in High-dimensions
by: Boopathy, Akhilan, et al.
Published: (2024)

Class-Balanced and Reinforced Active Learning on Graphs
by: Yu, Chengcheng, et al.
Published: (2024)

Balancing the Causal Effects in Class-Incremental Learning
by: Zheng, Junhao, et al.
Published: (2024)

Leveraging Influence Functions for Resampling Data in Physics-Informed Neural Networks
by: Naujoks, Jonas R., et al.
Published: (2025)

Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models
by: Chinodakufa, Tapiwa Amion, et al.
Published: (2026)

Resampling strategies for imbalanced regression: a survey and empirical analysis
by: Avelino, Juscimara G., et al.
Published: (2025)

Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization
by: Nagler, Thomas, et al.
Published: (2024)

Dataset Difficulty and the Role of Inductive Bias
by: Kwok, Devin, et al.
Published: (2024)

Resampling methods for private statistical inference
by: Chadha, Karan, et al.
Published: (2024)

vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvement
by: Zhu, Yiwen, et al.
Published: (2024)

Efficient Discovery of Significant Patterns with Few-Shot Resampling
by: Pellegrina, Leonardo, et al.
Published: (2024)

Differentiable Particle Filtering using Optimal Placement Resampling
by: Csuzdi, Domonkos, et al.
Published: (2024)

Controllable Generation via Locally Constrained Resampling
by: Ahmed, Kareem, et al.
Published: (2024)

Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection
by: Suvon, Mohammod N. I., et al.
Published: (2024)