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models optimization » model optimization (Expand Search), process optimization (Expand Search), wolf optimization (Expand Search)
field optimization » lead optimization (Expand Search), guided optimization (Expand Search), linear optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary risk » primary risk (Expand Search), dietary risk (Expand Search)
risk models » risk model (Expand Search)
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Showing 1 - 20 results of 53 for search '(( binary data field optimization algorithm ) OR ( binary risk models optimization algorithm ))', query time: 0.55s Refine Results
  1. 1
    Binary contingency table.

    Binary contingency table. by Lu Xiao (314030)

    Published 2024
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  2. 2
    Calibration curves for the test set of four models.

    Calibration curves for the test set of four models. by Meng Cao (105914)

    Published 2025
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  3. 3
    ROC curves for the test set of four models.

    ROC curves for the test set of four models. by Meng Cao (105914)

    Published 2025
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  4. 4
    Performance metrics of the models on the training and test set.

    Performance metrics of the models on the training and test set. by Meng Cao (105914)

    Published 2025
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  5. 5
    Parameter settings of the comparison algorithms.

    Parameter settings of the comparison algorithms. by Ying Li (38224)

    Published 2024
    “…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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  6. 6
    The Pseudo-Code of the IRBMO Algorithm.

    The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

    Published 2025
    “…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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  7. 7
    <i>hi</i>PRS algorithm process flow.

    <i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

    Published 2023
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  8. 8
    IRBMO vs. meta-heuristic algorithms boxplot.

    IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

    Published 2025
    “…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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  9. 9
    IRBMO vs. feature selection algorithm boxplot.

    IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

    Published 2025
    “…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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  10. 10
    SHAP bar plot.

    SHAP bar plot. by Meng Cao (105914)

    Published 2025
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  11. 11
    Display of the web prediction interface.

    Display of the web prediction interface. by Meng Cao (105914)

    Published 2025
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  12. 12
    Sample screening flowchart.

    Sample screening flowchart. by Meng Cao (105914)

    Published 2025
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  13. 13
    Descriptive statistics for variables.

    Descriptive statistics for variables. by Meng Cao (105914)

    Published 2025
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  14. 14
    SHAP summary plot.

    SHAP summary plot. by Meng Cao (105914)

    Published 2025
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  15. 15
    Datasets and their properties.

    Datasets and their properties. by Olaide N. Oyelade (14047002)

    Published 2023
    “…<div><p>Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. …”
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  16. 16
    Parameter settings.

    Parameter settings. by Olaide N. Oyelade (14047002)

    Published 2023
    “…<div><p>Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. …”
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  17. 17
    Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans.

    Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans. by Ramsés Noguez Imm (6658637)

    Published 2023
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  18. 18
    Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF

    Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF by Haidi Wang (9694004)

    Published 2020
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  19. 19
    Schematic representation of semi-automated algorithm for organoids analyzing.

    Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

    Published 2025
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  20. 20
    Comparison in terms of the sensitivity.

    Comparison in terms of the sensitivity. by Ying Li (38224)

    Published 2024
    “…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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