通过优化数据集提升模型分类性能，实现更高效训练。
Improving Model Classification by Optimizing the Training Dataset
- 引入可调参数优化核心数据集生成，包括确定性采样和类别分配。
- 在多个数据集上显著提升F1等分类指标，超越完整数据训练效果。
- 适合关注数据效率与分类精度的模型开发者使用。
在以数据为中心的人工智能时代，高质量训练数据的筛选与构建与模型设计同样关键。核心数据集（Coresets）提供了一种基于重要性采样的数据压缩方法，可在大规模数据上实现高效学习。然而，传统的基于敏感度的核心数据集构建方法往往侧重于损失逼近，而非直接优化分类性能指标（如F1分数）。本文提出一个系统性框架，用于调节核心数据集生成过程，以提升下游分类质量。新方法引入了可调参数，包括确定性采样、类别层面的样本分配，以及通过主动采样进行的迭代优化，超越了传统敏感度评分。在多种数据集和分类器上的大量实验表明，经过调优的核心数据集在关键分类指标上显著优于原始核心数据集及全量数据训练，为实现更高性能与更高效模型训练提供了有效路径。
原文摘要 · Abstract (English)
In the era of data-centric AI, the ability to curate high-quality training data is as crucial as model design. Coresets offer a principled approach to data reduction, enabling efficient learning on large datasets through importance sampling. However, conventional sensitivity-based coreset construction often falls short in optimizing for classification performance metrics, e.g., $F1$ score, focusing instead on loss approximation. In this work, we present a systematic framework for tuning the coreset generation process to enhance downstream classification quality. Our method introduces new tunable parameters--including deterministic sampling, class-wise allocation, and refinement via active sampling, beyond traditional sensitivity scores. Through extensive experiments on diverse datasets and classifiers, we demonstrate that tuned coresets can significantly outperform both vanilla coresets and full dataset training on key classification metrics, offering an effective path towards better and more efficient model training.
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