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    Active learning for regression (ALR) efficiently selects valuable unlabeled data for model training. A new passive sampling approach enhances data selection by considering representativeness and diversity, improving model performance.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Artificial Intelligence

    Background:

    • Active learning (AL) minimizes data labeling costs by selecting informative samples.
    • Pool-based sequential AL for regression (ALR) is crucial for efficient model development.

    Purpose of the Study:

    • To propose and evaluate criteria for effective ALR sample selection.
    • To introduce a novel passive sampling ALR approach.
    • To enhance existing ALR methods through integration.

    Main Methods:

    • Defined informativeness, representativeness, and diversity as key ALR criteria.
    • Compared four existing ALR methods against these criteria.
    • Developed a new ALR approach using passive sampling considering representativeness and diversity.
    • Integrated the new approach with existing ALR methods.

    Main Results:

    • The proposed passive sampling ALR approach demonstrated effectiveness.
    • Integration of the new approach improved performance of existing ALR methods.
    • Experiments across diverse datasets validated the proposed ALR strategies.

    Conclusions:

    • The novel passive sampling ALR approach offers significant improvements in data selection.
    • The proposed criteria provide a framework for evaluating ALR methods.
    • This work advances the field of active learning for regression tasks.