Adaptive Label Error Detection: A Bayesian Approach to Mislabeled Data Detection

2026-01-15

AI Summary

Label noise significantly degrades model performance, necessitating effective detection methods. This work proposes Adaptive Label Error Detection (ALED), which first extracts and denoises intermediate features from deep convolutional networks, then models each class as a multivariate Gaussian distribution on a low-dimensional manifold, and finally identifies mislabeled samples via a Bayesian likelihood ratio test. ALED is the first approach to integrate feature denoising, class-conditional Gaussian modeling, and likelihood ratio testing within a unified framework. Evaluated on multiple medical imaging datasets, it substantially outperforms existing techniques; fine-tuning models with labels corrected by ALED reduces test error rates by 33.8%, achieving both high sensitivity and precision.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Machine learning classification systems are susceptible to poor performance when trained with incorrect ground truth labels, even when data is well-curated by expert annotators. As machine learning becomes more widespread, it is increasingly imperative to identify and correct mislabeling to develop more powerful models. In this work, we motivate and describe Adaptive Label Error Detection (ALED), a novel method of detecting mislabeling. ALED extracts an intermediate feature space from a deep convolutional neural network, denoises the features, models the reduced manifold of each class with a multidimensional Gaussian distribution, and performs a simple likelihood ratio test to identify mislabeled samples. We show that ALED has markedly increased sensitivity, without compromising precision, compared to established label error detection methods, on multiple medical imaging datasets. We demonstrate an example where fine-tuning a neural network on corrected data results in a 33.8% decrease in test set errors, providing strong benefits to end users. The ALED detector is deployed in the Python package statlab.

Problem

• label error detection
• mislabeled data
• classification
• data quality
• machine learning

Innovation

• Adaptive Label Error Detection
• Bayesian approach
• feature denoising
• Gaussian class modeling
• likelihood ratio test