Search alternatives:
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
used optimization » based optimization (Expand Search), led optimization (Expand Search), guided 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 data » primary data (Expand Search), dietary data (Expand Search)
Showing 21 - 40 results of 278 for search '(( binary space feature optimization algorithm ) OR ( binary data used optimization algorithm ))', query time: 0.55s Refine Results
  1. 21
    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
    Subjects:
    Save to List
    Saved in:
  2. 22
    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. …”
    Save to List
    Saved in:
  3. 23
    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. …”
    Save to List
    Saved in:
  4. 24
    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. …”
    Save to List
    Saved in:
  5. 25
    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. …”
    Save to List
    Saved in:
  6. 26
    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. …”
    Save to List
    Saved in:
  7. 27
    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. …”
    Save to List
    Saved in:
  8. 28
    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. …”
    Save to List
    Saved in:
  9. 29
    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. …”
    Save to List
    Saved in:
  10. 30
    Best fitness value results for tested algorithms.

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

    Published 2022
    Subjects:
    Save to List
    Saved in:
  11. 31
    Mean fitness value results for tested algorithms.

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

    Published 2022
    Subjects:
    Save to List
    Saved in:
  12. 32
    Standard deviation of fitness value results for tested algorithms.

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

    Published 2022
    Subjects:
    Save to List
    Saved in:
  13. 33
    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
    Save to List
    Saved in:
  14. 34
    Flowchart depicting the structure of the BDMO algorithm.

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

    Published 2022
    Save to List
    Saved in:
  15. 35
    The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms.

    The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

    Published 2023
    Subjects:
    Save to List
    Saved in:
  16. 36
    Control parameter settings for comparative feature selection methods.

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

    Published 2022
    Subjects:
    Save to List
    Saved in:
  17. 37
    IRBMO vs. variant comparison adaptation data.

    IRBMO vs. variant comparison adaptation data. 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. …”
    Save to List
    Saved in:
  18. 38
    One advantage of our algorithm is that it is not binary in nature.

    One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

    Published 2022
    Save to List
    Saved in:
  19. 39
    Pseudo Code of RBMO.

    Pseudo Code of RBMO. 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. …”
    Save to List
    Saved in:
  20. 40
    P-value on CEC-2017(Dim = 30).

    P-value on CEC-2017(Dim = 30). 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. …”
    Save to List
    Saved in:

Search Tools: