A Simulation-Based Approach for Quantifying the Impact of Interactive Label Correction for Machine Learning

Recent years have witnessed growing interest in understanding the sensitivity of machine learning to training data characteristics. While researchers have claimed the benefits of activities such as a human-in-the-loop approach of interactive label correction for improving model performance, there have been limited studies to quantitatively probe the relationship between the cost of label correction and the associated benefit in model performance. We employ a simulation-based approach to explore the efficacy of label correction under diverse task conditions, namely different datasets, noise properties, and machine learning algorithms. We measure the impact of label correction on model performance under the best-case scenario assumption: perfect correction (perfect human and visual systems), serving as an upper-bound estimation of the benefits derived from visual interactive label correction. The simulation results reveal a trade-off between the label correction effort expended and model performance improvement. Notably, task conditions play a crucial role in shaping the trade-off. Based on the simulation results, we develop a set of recommendations to help practitioners determine conditions under which interactive label correction is an effective mechanism for improving model performance.