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

2025-07-22

AI Summary

To address model bias arising from severe class imbalance in tabular data—such as in fraud detection and medical diagnosis—where minority-class samples are critically scarce, this paper proposes an AI-driven oversampling method based on synthetic data generation. Unlike conventional oversampling techniques, our approach leverages the open-source MOSTLY AI Synthetic Data SDK to generate high-fidelity, mixed-type synthetic instances that precisely populate sparse regions of the minority class in feature space, thereby enhancing distributional diversity and representativeness. Extensive experiments on multiple real-world imbalanced datasets demonstrate that the proposed method consistently outperforms baseline oversamplers—including SMOTE and ADASYN—across key metrics such as F1-score, AUC, and G-mean. Notably, performance gains are most pronounced under extreme imbalance (minority class < 1%), underscoring the efficacy and generalizability of synthetic-data-driven oversampling for mitigating class bias.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Ensemble Methods

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Addressing under-representation of minority classes in unbalanced data
• Evaluating synthetic data upsampling for improving predictive accuracy
• Comparing synthetic upsampling with traditional methods like SMOTE-NC

Innovation

• Open-source synthetic data upsampling for unbalanced datasets
• AI-generated synthetic data improves minority class accuracy
• Flexible synthetic upsampling outperforms traditional methods