A Comprehensive Exploitation of Instance Selection Methods for Automatic Text Classification — “Doing More with Less”

• Washington Cunha Federal University of Minas Gerais (UFMG)
• Leonardo Rocha Federal University of São João del-Rei (UFSJ)
• Marcos A. Gonçalves Federal University of Minas Gerais (UFMG)

DOI: https://doi.org/10.5753/sbbd_estendido.2025.247534

Abstract

Recent progress in NLP has followed a “more is better” trend (more data, computing power, and model complexity) best exemplified by the Large Language Models (LLMs). However, training such models remains resource-intensive. This Ph.D. dissertation explores Instance Selection (IS), a promising yet underexplored data engineering technique that reduces training set size by removing noisy or redundant instances, lowering computational cost without sacrificing performance. We evaluate comprehensively the IS methods for Automatic Text Classification (ATC) across several classifiers and 22 datasets, uncovering significant untapped potential. Additionally, we propose two novel IS methods tailored for large datasets and LLMs. Our best solution cut training set sizes by 41% on average while preserving effectiveness, and achieved speedups of up to 2.46x, proving its scalability.

Keywords: instance selection, automatic text classification, large language models, green computing

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