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Updated: Jun 17, 2025

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Label Consistency-Based Ground Truth Inference for Crowdsourcing.

Jiao Li, Liangxiao Jiang, Wenjun Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |August 14, 2024
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    Summary
    This summary is machine-generated.

    This study introduces a novel label selection strategy for crowdsourcing, outperforming traditional aggregation methods. The proposed method, label consistency-based ground truth inference (LCGTI), identifies the best worker label for accurate ground truth inference.

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    Area of Science:

    • Data Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Crowdsourcing generates multiple noisy labels per instance.
    • Existing methods aggregate noisy labels for ground truth inference.
    • This aggregation approach may not yield optimal results.

    Purpose of the Study:

    • To introduce a new strategy: label selection for ground truth inference.
    • To propose a label consistency-based ground truth inference (LCGTI) method.
    • To evaluate LCGTI against state-of-the-art label aggregation methods.

    Main Methods:

    • LCGTI estimates worker quality based on label consistency.
    • Bias is measured by consistency between workers on the same instances.
    • Variance is measured by consistency of a worker on similar instances.
    • Worker quality is combined to select the highest quality label as ground truth.

    Main Results:

    • LCGTI was evaluated on 34 simulated and 2 real-world datasets.
    • The proposed LCGTI method significantly outperformed existing label aggregation methods.
    • Label selection proved more effective than label aggregation for ground truth inference.

    Conclusions:

    • LCGTI offers a superior approach to ground truth inference in crowdsourcing.
    • The label selection strategy effectively leverages worker quality.
    • This method enhances the accuracy of ground truth determination from noisy crowdsourced labels.