Search alternatives:
models optimization » model optimization (Expand Search), process optimization (Expand Search), wolf optimization (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
based driven » based diet (Expand Search), wave driven (Expand Search), user driven (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
lines based » lens based (Expand Search), genes based (Expand Search), lines used (Expand Search)
Showing 1 - 20 results of 183 for search '(( binary data models optimization algorithm ) OR ( lines based driven optimization algorithm ))', query time: 1.16s 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
    Save to List
    Saved in:
  2. 2
    Specificity values of IRBMO vs. other binary algorithms.

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

    Published 2025
    Save to List
    Saved in:
  3. 3
    Fitness values of IRBMO vs. other binary algorithms.

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

    Published 2025
    Save to List
    Saved in:
  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
    Save to List
    Saved in:
  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
    Save to List
    Saved in:
  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
    Save to List
    Saved in:
  7. 7
    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
    Save to List
    Saved in:
  8. 8
    Binary contingency table.

    Binary contingency table. by Lu Xiao (314030)

    Published 2024
    Save to List
    Saved in:
  9. 9
    RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values .

    RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values . by Jakub Stoklosa (3315042)

    Published 2023
    Save to List
    Saved in:
  10. 10
    Top-performing model configuration and performance by algorithm type.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  11. 11
    Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information.

    Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

    Published 2024
    Save to List
    Saved in:
  12. 12
    Confusion matrix for a binary outcome (adapted from Fawcett [30]).

    Confusion matrix for a binary outcome (adapted from Fawcett [30]). by João Albuquerque (12936572)

    Published 2022
    Save to List
    Saved in:
  13. 13
    Complex traffic line and track point diagram.

    Complex traffic line and track point diagram. by Mingkang Sun (6498791)

    Published 2024
    Save to List
    Saved in:
  14. 14
    Complex traffic line and track point diagram.

    Complex traffic line and track point diagram. by Mingkang Sun (6498791)

    Published 2024
    Save to List
    Saved in:
  15. 15
    If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions.

    If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

    Published 2024
    Save to List
    Saved in:
  16. 16
    Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data

    Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

    Published 2022
    “…In this study, the effects of CI and data scarcity (DS) on the performance of binary classification models were investigated using ToxCast bioassay data. …”
    Save to List
    Saved in:
  17. 17
    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
    Subjects:
    Save to List
    Saved in:
  18. 18
    The prediction accuracy of simple versus hybrid models.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  19. 19
    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. …”
    Save to List
    Saved in:
  20. 20
    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
    Save to List
    Saved in:

Search Tools: