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Updated: Jun 12, 2025

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

Yixuan Wang, Jieqiong Zhao, Jiayi Hong

    IEEE Transactions on Visualization and Computer Graphics
    |September 26, 2024
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    Summary
    This summary is machine-generated.

    Interactive label correction can improve machine learning model performance, but the benefit depends on the effort invested and specific task conditions. This study quantifies this trade-off for optimal data labeling strategies.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Human-Computer Interaction

    Background:

    • Growing interest in machine learning (ML) sensitivity to training data.
    • Claimed benefits of human-in-the-loop (HITL) for ML performance, like interactive label correction.
    • Limited quantitative studies on the cost-benefit relationship of label correction.

    Purpose of the Study:

    • To quantitatively explore the efficacy of label correction in ML.
    • To investigate the trade-off between label correction cost and model performance gains.
    • To provide recommendations for effective interactive label correction strategies.

    Main Methods:

    • Simulation-based approach to assess label correction.
    • Evaluation across diverse datasets, noise properties, and ML algorithms.
    • Analysis under a best-case scenario of perfect correction for upper-bound benefit estimation.

    Main Results:

    • A clear trade-off exists between label correction effort and model performance improvement.
    • Task conditions significantly influence the observed trade-off.
    • The study establishes an upper-bound estimation for benefits from visual interactive label correction.

    Conclusions:

    • Interactive label correction is effective, but its value is context-dependent.
    • Task conditions are critical in determining the success of label correction.
    • Recommendations are provided to guide practitioners in applying label correction effectively.