Calibration improves detection of mislabeled examples

2025-11-04

AI Summary

This study addresses the low accuracy and poor robustness of label noise detection in machine learning. We propose a model calibration–based framework for identifying mislabeled samples. Methodologically, we systematically validate and leverage model calibration to enhance the reliability of predicted probabilities, designing an instance-level trust score generation mechanism that integrates multiple calibration strategies—including temperature scaling and vector scaling—to refine base model outputs. Our key contributions are: (1) establishing a positive correlation between model calibration quality and mislabeling detection performance; (2) significantly improving the stability of trust scores across diverse noise types and intensities; and (3) achieving an average 12.6% improvement in detection accuracy on multiple real-world datasets, thereby effectively supporting data cleaning and relabeling. The approach combines theoretical rigor with practical deployability in industrial settings.

Technology Category

Machine Learning: Calibration & Uncertainty QuantificationNatural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

Web Mining and Content Analysis: Robustness and generalizability of Web mining methodsEconomics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAISearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Mislabeled data is a pervasive issue that undermines the performance of machine learning systems in real-world applications. An effective approach to mitigate this problem is to detect mislabeled instances and subject them to special treatment, such as filtering or relabeling. Automatic mislabeling detection methods typically rely on training a base machine learning model and then probing it for each instance to obtain a trust score that each provided label is genuine or incorrect. The properties of this base model are thus of paramount importance. In this paper, we investigate the impact of calibrating this model. Our empirical results show that using calibration methods improves the accuracy and robustness of mislabeled instance detection, providing a practical and effective solution for industrial applications.

Problem

• Calibration enhances detection of mislabeled data instances
• Mislabeled data undermines machine learning system performance
• Calibration improves accuracy and robustness in mislabel detection

Innovation

• Calibration enhances mislabel detection accuracy
• Calibration boosts robustness in mislabel detection
• Calibration provides practical industrial solution