Improving Model Classification by Optimizing the Training Dataset

2025-07-22

AI Summary

This work addresses the limitation of conventional dataset optimization methods, which overly rely on loss approximation and neglect downstream classification metrics. We propose a systematic coreset construction framework explicitly designed to enhance classification performance. Methodologically, it integrates deterministic sampling, class-level importance weighting, and active learning–based refinement, optimizing directly for task-oriented metrics such as the F1 score—thereby overcoming the narrow focus of sensitivity-based sampling on gradient or loss approximation alone. Our key innovation lies in the joint design of importance sampling and tunable parameters to enable end-to-end optimization of training data quality. Extensive experiments across multiple benchmark datasets and classifiers demonstrate that our approach significantly outperforms standard coreset baselines and even full-data training, achieving superior classification accuracy while maintaining high training efficiency.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Optimization
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Optimizing coresets to improve classification performance metrics
• Enhancing model training efficiency via tuned data reduction
• Addressing limitations of sensitivity-based coreset construction methods

Innovation

• Tunable coreset generation for classification enhancement
• Deterministic and class-wise sampling strategies
• Active sampling refinement beyond sensitivity scores