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Showing 1 - 20 results of 35 for search '(( binary most based optimization algorithm ) OR ( library based codon optimization algorithm ))*', query time: 0.57s Refine Results
  1. 1
    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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  2. 2
    Datasets and their properties.

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

    Published 2023
    “…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …”
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  3. 3
    Parameter settings.

    Parameter settings. by Olaide N. Oyelade (14047002)

    Published 2023
    “…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …”
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  4. 4
    Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data

    Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data by Fei Xue (24567)

    Published 2021
    “…In this article, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. …”
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  5. 5
    SHAP bar plot.

    SHAP bar plot. by Meng Cao (105914)

    Published 2025
    “…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…”
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  6. 6
    Sample screening flowchart.

    Sample screening flowchart. by Meng Cao (105914)

    Published 2025
    “…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…”
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  7. 7
    Descriptive statistics for variables.

    Descriptive statistics for variables. by Meng Cao (105914)

    Published 2025
    “…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…”
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  8. 8
    SHAP summary plot.

    SHAP summary plot. by Meng Cao (105914)

    Published 2025
    “…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…”
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  9. 9
    ROC curves for the test set of four models.

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

    Published 2025
    “…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…”
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  10. 10
    Display of the web prediction interface.

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

    Published 2025
    “…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…”
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  11. 11
    Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19.

    Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. by Jiaqing Luo (10975030)

    Published 2021
    “…<p>(A). The MCDM algorithm-Stage 1. Preprocessing, this stage is the process of refining the collected raw data to eliminate noise, including correlation analysis and feature selection based on P values. …”
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  12. 12
    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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  13. 13
    Parameter sensitivity of BIMGO.

    Parameter sensitivity of BIMGO. 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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  14. 14
    Details of the medical datasets.

    Details of the medical datasets. 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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  15. 15
    The flowchart of IMGO.

    The flowchart of IMGO. 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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  16. 16
    Comparison in terms of the selected features.

    Comparison in terms of the selected features. 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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  17. 17
    Iterative chart of control factor.

    Iterative chart of control factor. 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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  18. 18
    Details of 23 basic benchmark functions.

    Details of 23 basic benchmark functions. 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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  19. 19
    Related researches.

    Related researches. 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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  20. 20
    S1 Dataset -

    S1 Dataset - 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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