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Showing 1 - 20 results of 55 for search '(( primary data wolf optimization algorithm ) OR ( binary made based optimization algorithm ))', query time: 0.50s Refine Results
  1. 1
    S1 Data -

    S1 Data - by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  2. 2
    Parameter settings for algorithms.

    Parameter settings for algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  3. 3
    Parameter settings for algorithms.

    Parameter settings for algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  4. 4
    Average runtime of different algorithms.

    Average runtime of different algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  5. 5
    Average runtime of different algorithms.

    Average runtime of different algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  6. 6
    Flowchart of GJO-GWO algorithm.

    Flowchart of GJO-GWO algorithm. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  7. 7
    Algorithm for generating hyperparameter.

    Algorithm for generating hyperparameter. by Balraj Preet Kaur (20370832)

    Published 2024
    “…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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  8. 8
    Results of machine learning algorithm.

    Results of machine learning algorithm. by Balraj Preet Kaur (20370832)

    Published 2024
    “…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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  9. 9
    ROC comparison of machine learning algorithm.

    ROC comparison of machine learning algorithm. by Balraj Preet Kaur (20370832)

    Published 2024
    “…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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  10. 10
    Detailed information of benchmark functions.

    Detailed information of benchmark functions. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  11. 11
    Evaluation metrics of the models’ performance.

    Evaluation metrics of the models’ performance. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  12. 12
    Detailed information of datasets.

    Detailed information of datasets. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  13. 13
    Friedman test results.

    Friedman test results. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  14. 14
    Average number of selected features.

    Average number of selected features. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  15. 15
    Wilcoxon rank sum test results.

    Wilcoxon rank sum test results. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  16. 16
    Wilcoxon rank sum test results.

    Wilcoxon rank sum test results. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  17. 17
    Average number of selected features.

    Average number of selected features. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
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  18. 18
    Best optimizer results of Lightbgm.

    Best optimizer results of Lightbgm. by Balraj Preet Kaur (20370832)

    Published 2024
    “…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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  19. 19
    Best optimizer results of Adaboost.

    Best optimizer results of Adaboost. by Balraj Preet Kaur (20370832)

    Published 2024
    “…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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  20. 20
    Best optimizer results of Lightbgm.

    Best optimizer results of Lightbgm. by Balraj Preet Kaur (20370832)

    Published 2024
    “…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”
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