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feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
basis optimization » based optimization (Expand Search), task optimization (Expand Search), acid optimization (Expand Search)
space feature » scale feature (Expand Search), scale features (Expand Search), shape features (Expand Search)
binary space » binary image (Expand Search), banach space (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
Showing 1 - 20 results of 100 for search '(( binary space feature optimization algorithm ) OR ( binary based basis optimization algorithm ))', query time: 0.65s Refine Results
  1. 1
    Feature selection convergence time comparison of various binary metaheuristic algorithms.

    Feature selection convergence time comparison of various binary metaheuristic algorithms. by Amal H. Alharbi (21755906)

    Published 2025
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  2. 2
    Number of features selected for IRBMO vs. other binary algorithms.

    Number of features selected for IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  3. 3
    Sensitivity values of IRBMO vs. other binary algorithms.

    Sensitivity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  4. 4
    Specificity values of IRBMO vs. other binary algorithms.

    Specificity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  5. 5
    Fitness values of IRBMO vs. other binary algorithms.

    Fitness values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  6. 6
    Classification accuracy values of IRBMO vs. other binary algorithms.

    Classification accuracy values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  7. 7
    F-score values of IRBMO vs. other binary algorithms.

    F-score values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  8. 8
    Feature selection ratio results for tested algorithms.

    Feature selection ratio results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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  9. 9
    The result of the accuracy of tested algorithms.

    The result of the accuracy of tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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  10. 10
    Initial parameter settings for the optimization algorithms.

    Initial parameter settings for the optimization algorithms. by Doaa Sami Khafaga (21463870)

    Published 2025
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  11. 11
    Bar chart comparing the average fitness values of various feature selection algorithms.

    Bar chart comparing the average fitness values of various feature selection algorithms. by Doaa Sami Khafaga (21463870)

    Published 2025
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  12. 12
    Numerical example for determining the optimal number of clusters in Algorithm 2.

    Numerical example for determining the optimal number of clusters in Algorithm 2. by Yi Wei Tye (22208735)

    Published 2025
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  13. 13
    a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset.

    a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. by Nebojsa Bacanin (13932723)

    Published 2022
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  14. 14
    Best fitness value results for tested algorithms.

    Best fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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  15. 15
    Mean fitness value results for tested algorithms.

    Mean fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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  16. 16
    Standard deviation of fitness value results for tested algorithms.

    Standard deviation of fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
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  17. 17
    Box plots with swarm overlays for six feature selection algorithms, annotated with mean and standard deviation values.

    Box plots with swarm overlays for six feature selection algorithms, annotated with mean and standard deviation values. by Doaa Sami Khafaga (21463870)

    Published 2025
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  18. 18
    Flowchart depicting the structure of the BDMO algorithm.

    Flowchart depicting the structure of the BDMO algorithm. by Olatunji A. Akinola (13919696)

    Published 2022
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  19. 19
    Control parameter settings for comparative feature selection methods.

    Control parameter settings for comparative feature selection methods. by Nebojsa Bacanin (13932723)

    Published 2022
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  20. 20
    Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment

    Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

    Published 2019
    “…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …”
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