Improving Model Classification by Optimizing the Training Dataset

• URL: http://arxiv.org/abs/2507.16729v1
• Date: Tue, 22 Jul 2025 16:10:11 GMT
• Title: Improving Model Classification by Optimizing the Training Dataset
• Authors: Morad Tukan, Loay Mualem, Eitan Netzer, Liran Sigalat,
• Abstract summary: Coresets offer a principled approach to data reduction, enabling efficient learning on large datasets.<n>We present a systematic framework for tuning the coreset generation process to enhance downstream classification quality.
• Score: 3.987352341101438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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