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Updated: Jul 2, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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EvoVis: A Visual Analytics Method to Understand the Labeling Iterations in Data Programming.

Sisi Li, Guanzhong Liu, Tianxiang Wei

    IEEE Transactions on Visualization and Computer Graphics
    |February 28, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Data programming faces challenges in understanding labeling iterations. EvoVis, a visual analytics method, aids data programmers in improving labeled data quality and optimizing labeling functions (LFs).

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    Area of Science:

    • Machine Learning
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • High-quality labeled training data is crucial for machine learning but obtaining it is a significant bottleneck.
    • Data programming offers a solution by using labeling functions (LFs) to generate probabilistic labels from human knowledge.
    • Iteratively refining LFs is common but understanding these iterations is complex due to intricate relationships and data scale.

    Purpose of the Study:

    • To introduce EvoVis, a visual analytics method designed to explain labeling iterations in data programming.
    • To address the challenges in evaluating label quality and optimizing LFs for multi-class text labeling tasks.

    Main Methods:

    • EvoVis integrates relationship analysis and temporal overview for contextual and historical information display.
    • The method was assessed through case studies and user studies to evaluate its utility and effectiveness.

    Main Results:

    • EvoVis effectively assists data programmers in understanding labeling iterations.
    • The method aids in improving the quality of labeled data.
    • A notable increase of 0.16 in the average F1 score was observed compared to default analysis tools.

    Conclusions:

    • EvoVis enhances the understanding of labeling iterations in data programming.
    • The visual analytics approach contributes to improving the quality and optimization of labeled data.
    • EvoVis demonstrates practical utility in addressing key challenges in the data programming paradigm.