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

• URL: http://arxiv.org/abs/2507.16419v1
• Date: Tue, 22 Jul 2025 10:11:32 GMT
• Title: Improving Predictions on Highly Unbalanced Data Using Open Source Synthetic Data Upsampling
• Authors: Ivona Krchova, Michael Platzer, Paul Tiwald,
• Abstract summary: We show that synthetic data can improve predictive accuracy for minority groups by generating diverse data points that fill gaps in sparse regions of the feature space.<n>We evaluate the effectiveness of an open-source solution, the Synthetic Data SDK by MOSTLY AI, which provides a flexible and user-friendly approach to synthetic upsampling for mixed-type data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unbalanced tabular data sets present significant challenges for predictive modeling and data analysis across a wide range of applications. In many real-world scenarios, such as fraud detection, medical diagnosis, and rare event prediction, minority classes are vastly underrepresented, making it difficult for traditional machine learning algorithms to achieve high accuracy. These algorithms tend to favor the majority class, leading to biased models that struggle to accurately represent minority classes. Synthetic data holds promise for addressing the under-representation of minority classes by providing new, diverse, and highly realistic samples. This paper presents a benchmark study on the use of AI-generated synthetic data for upsampling highly unbalanced tabular data sets. We evaluate the effectiveness of an open-source solution, the Synthetic Data SDK by MOSTLY AI, which provides a flexible and user-friendly approach to synthetic upsampling for mixed-type data. We compare predictive models trained on data sets upsampled with synthetic records to those using standard methods, such as naive oversampling and SMOTE-NC. Our results demonstrate that synthetic data can improve predictive accuracy for minority groups by generating diverse data points that fill gaps in sparse regions of the feature space. We show that upsampled synthetic training data consistently results in top-performing predictive models, particularly for mixed-type data sets containing very few minority samples.

Related papers

• Less is More: Adaptive Coverage for Synthetic Training Data [20.136698279893857]
  This study introduces a novel sampling algorithm, based on the maximum coverage problem, to select a representative subset from a synthetically generated dataset.<n>Our results demonstrate that training a classifier on this contextually sampled subset achieves superior performance compared to training on the entire dataset.
  arXiv  Detail & Related papers  (2025-04-20T06:45:16Z)
• A CLIP-Powered Framework for Robust and Generalizable Data Selection [51.46695086779598]
  Real-world datasets often contain redundant and noisy data, imposing a negative impact on training efficiency and model performance.<n>Data selection has shown promise in identifying the most representative samples from the entire dataset.<n>We propose a novel CLIP-powered data selection framework that leverages multimodal information for more robust and generalizable sample selection.
  arXiv  Detail & Related papers  (2024-10-15T03:00:58Z)
• Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance [16.047084318753377]
  Imbalanced classification and spurious correlation are common challenges in data science and machine learning.<n>Recent advances have proposed leveraging the flexibility and generative capabilities of large language models to generate synthetic samples.<n>This article develops novel theoretical foundations to systematically study the roles of synthetic samples in addressing imbalanced classification and spurious correlation.
  arXiv  Detail & Related papers  (2024-06-05T21:24:26Z)
• Synthetic Information towards Maximum Posterior Ratio for deep learning on Imbalanced Data [1.7495515703051119]
  We propose a technique for data balancing by generating synthetic data for the minority class. Our method prioritizes balancing the informative regions by identifying high entropy samples. Our experimental results on forty-one datasets demonstrate the superior performance of our technique.
  arXiv  Detail & Related papers  (2024-01-05T01:08:26Z)
• Learning Defect Prediction from Unrealistic Data [57.53586547895278]
  Pretrained models of code have become popular choices for code understanding and generation tasks. Such models tend to be large and require commensurate volumes of training data. It has become popular to train models with far larger but less realistic datasets, such as functions with artificially injected bugs. Models trained on such data tend to only perform well on similar data, while underperforming on real world programs.
  arXiv  Detail & Related papers  (2023-11-02T01:51:43Z)
• Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models [69.76066070227452]
  *Data Synthesis* is a promising way to train a small model with very little labeled data. We propose *Synthesis Step by Step* (**S3**), a data synthesis framework that shrinks this distribution gap. Our approach improves the performance of a small model by reducing the gap between the synthetic dataset and the real data.
  arXiv  Detail & Related papers  (2023-10-20T17:14:25Z)
• Tackling Diverse Minorities in Imbalanced Classification [80.78227787608714]
  Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. We propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-28T18:48:34Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• Generative Oversampling for Imbalanced Data via Majority-Guided VAE [15.93867386081279]
  We propose a novel over-sampling model, called Majority-Guided VAE(MGVAE), which generates new minority samples under the guidance of a majority-based prior. In this way, the newly generated minority samples can inherit the diversity and richness of the majority ones, thus mitigating overfitting in downstream tasks.
  arXiv  Detail & Related papers  (2023-02-14T06:35:23Z)
• Synthesising Multi-Modal Minority Samples for Tabular Data [3.7311680121118345]
  Adding synthetic minority samples to the dataset before training is a popular technique to address this difficulty. We propose a latent space framework which maps the multi-modal samples to a dense continuous latent space. We show that our framework generates better synthetic data than the existing methods.
  arXiv  Detail & Related papers  (2021-05-17T23:54:08Z)
• Towards Synthetic Multivariate Time Series Generation for Flare Forecasting [5.098461305284216]
  One of the limiting factors in training data-driven, rare-event prediction algorithms is the scarcity of the events of interest. In this study, we explore the usefulness of the conditional generative adversarial network (CGAN) as a means to perform data-informed oversampling.
  arXiv  Detail & Related papers  (2021-05-16T22:23:23Z)
• An Investigation of Why Overparameterization Exacerbates Spurious Correlations [98.3066727301239]
  We identify two key properties of the training data that drive this behavior. We show how the inductive bias of models towards "memorizing" fewer examples can cause over parameterization to hurt.
  arXiv  Detail & Related papers  (2020-05-09T01:59:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.