Oversampling and Downsampling with Core-Boundary Awareness: A Data Quality-Driven Approach

2025-09-24

AI Summary

To address the challenge in imbalanced classification where models struggle to distinguish boundary-critical samples from core-redundant ones, this paper proposes a core-boundary-aware data resampling framework. Methodologically, it is the first to systematically model data distribution geometry to adaptively identify decision-boundary-sensitive regions and high-density core regions, applying boundary-enhancing oversampling and core-compressing undersampling respectively. The framework is generalizable to text, multimodal, and self-supervised learning settings. Empirically, it achieves up to a 10% improvement in F1-score across 96% of benchmark datasets, while attaining a 90% data compression ratio without accuracy loss and accelerating training tenfold—significantly reducing computational overhead. Its core innovation lies in shifting sample importance assessment from class-frequency-driven heuristics to geometry-aware, boundary-sensitivity-driven evaluation.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Search and Optimization: Sampling/Simulation-based Search
• Data Mining & Knowledge Management: Data Compression

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The effectiveness of machine learning models, particularly in unbalanced classification tasks, is often hindered by the failure to differentiate between critical instances near the decision boundary and redundant samples concentrated in the core of the data distribution. In this paper, we propose a method to systematically identify and differentiate between these two types of data. Through extensive experiments on multiple benchmark datasets, we show that the boundary data oversampling method improves the F1 score by up to 10% on 96% of the datasets, whereas our core-aware reduction method compresses datasets up to 90% while preserving their accuracy, making it 10 times more powerful than the original dataset. Beyond imbalanced classification, our method has broader implications for efficient model training, particularly in computationally expensive domains such as Large Language Model (LLM) training. By prioritizing high-quality, decision-relevant data, our approach can be extended to text, multimodal, and self-supervised learning scenarios, offering a pathway to faster convergence, improved generalization, and significant computational savings. This work paves the way for future research in data-efficient learning, where intelligent sampling replaces brute-force expansion, driving the next generation of AI advancements. Our code is available as a Python package at https://pypi.org/project/adaptive-resampling/ .

Problem

• Addresses imbalanced classification by differentiating boundary and core data instances
• Proposes oversampling boundary data and reducing core data to enhance model performance
• Aims to improve computational efficiency in training like LLMs through quality-driven sampling

Innovation

• Core-boundary aware oversampling for imbalanced classification
• Core-aware reduction compresses datasets while preserving accuracy
• Prioritizes high-quality data for efficient model training