Mislabel Detection

Mislabel detection focuses on identifying incorrectly labeled data points within datasets used to train machine learning models, a critical step for improving model accuracy and reliability. Current research emphasizes developing robust and efficient algorithms, often employing techniques like neighborhood analysis and Boltzmann influence functions, to detect these errors across various data types (images, graphs, tabular data). Effective mislabel detection is crucial for enhancing the trustworthiness and performance of machine learning models across diverse applications, ranging from computer vision to healthcare and autonomous systems, by improving data quality and mitigating the negative impact of noisy labels.