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A Method for Identifying Ground Truth Labels in Regression Problems using Annotator Precision.

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    This study introduces a new method for creating ground truth labels in regression tasks. It weights annotator precision, ensuring more accurate annotators have a greater impact on results.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Science

    Background:

    • Accurate ground truth labeling is crucial for supervised machine learning regression tasks.
    • Conventional methods often assume uniform annotator precision, potentially introducing noise.
    • Existing approaches may not adequately leverage varying levels of annotator expertise.

    Purpose of the Study:

    • To introduce a novel method for deriving ground truth labels in regression problems.
    • To account for individual annotator precision on a per-label basis.
    • To compare the proposed precision-weighted method against traditional global mean approaches.

    Main Methods:

    • Developed a novel algorithm that calculates ground truth labels by considering individual annotator precision.
    • Implemented a weighting scheme where higher-performing annotators contribute more significantly to the final landmark position.
    • Designed preliminary experiments to evaluate the method's performance.

    Main Results:

    • The precision-based method demonstrated a more nuanced approach to ground truth derivation compared to the global mean.
    • Preliminary results suggest that weighting annotator contributions improves the quality of derived labels.
    • The novel method shows potential for enhancing the reliability of regression model training data.

    Conclusions:

    • The proposed method offers an improvement over conventional techniques by acknowledging differential annotator accuracy.
    • This approach can lead to more robust and reliable ground truth datasets for regression tasks.
    • Further research is warranted to explore the full potential and applicability of precision-weighted labeling.