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derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), wolf optimization (Expand Search)
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Showing 1 - 20 results of 57 for search '(( binary task derived optimization algorithm ) OR ( binary data model optimization algorithm ))', query time: 0.70s Refine Results
  1. 1
    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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  2. 2
    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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  3. 3
    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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  4. 4
    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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  5. 5
    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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  6. 6
    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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  7. 7
    Binary contingency table.

    Binary contingency table. by Lu Xiao (314030)

    Published 2024
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  8. 8
    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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  9. 9
    Top-performing model configuration and performance by algorithm type.

    Top-performing model configuration and performance by algorithm type. by Uğur Ejder (22683228)

    Published 2025
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  10. 10
    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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  11. 11
    Flow diagram of the proposed model.

    Flow diagram of the proposed model. by Uğur Ejder (22683228)

    Published 2025
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  12. 12
    Convergence curve of feature selection algorithms across 11 datasets with KNN classifier.

    Convergence curve of feature selection algorithms across 11 datasets with KNN classifier. by Yi Wei Tye (22208735)

    Published 2025
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  13. 13
    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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  14. 14
    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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  15. 15
    The comparison of the accuracy score of the benchmark and the proposed models.

    The comparison of the accuracy score of the benchmark and the proposed models. by Uğur Ejder (22683228)

    Published 2025
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  16. 16
    The prediction accuracy of simple versus hybrid models.

    The prediction accuracy of simple versus hybrid models. by Uğur Ejder (22683228)

    Published 2025
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  17. 17
    The accuracy score change matrix following model development.

    The accuracy score change matrix following model development. by Uğur Ejder (22683228)

    Published 2025
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  18. 18
    The statistical description of the original data set of the patients (<i>n</i> = 162).

    The statistical description of the original data set of the patients (<i>n</i> = 162). by Uğur Ejder (22683228)

    Published 2025
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  19. 19
    The list of parameters of the modified data set for machine learning (<i>n</i> = 162).

    The list of parameters of the modified data set for machine learning (<i>n</i> = 162). by Uğur Ejder (22683228)

    Published 2025
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  20. 20
    Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%).

    Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%). by Uğur Ejder (22683228)

    Published 2025
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