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Showing 1 - 20 results of 678 for search '(( class linear decrease ) OR ( _ largest decrease ))', query time: 0.31s Refine Results
  1. 1
    Investigating the trend of land use class changes.

    Investigating the trend of land use class changes. by Abdolhossein Boali (19996390)

    Published 2024
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  2. 2
    Class imbalance on GMSC dataset.

    Class imbalance on GMSC dataset. by Seongil Han (20490232)

    Published 2024
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  3. 3
    Largest changes in influenza vaccine rates, Pre- vs. Post-COVID-19.

    Largest changes in influenza vaccine rates, Pre- vs. Post-COVID-19. by Tyler B. Nofzinger (21560244)

    Published 2025
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  4. 4
    Class distribution balanced through SMOTE oversampling.

    Class distribution balanced through SMOTE oversampling. by Seongil Han (20490232)

    Published 2024
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  5. 5
    Class distribution balanced through NearMiss undersampling.

    Class distribution balanced through NearMiss undersampling. by Seongil Han (20490232)

    Published 2024
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  6. 6
    Based on our time series data(first from the left), the visualizations of linear residual predictions under different decimation rates (next three) show that as the decimation rate decreases, the range of residual variations also becomes smaller.

    Based on our time series data(first from the left), the visualizations of linear residual predictions under different decimation rates (next three) show that as the decimation rate... by Hua Huo (1818133)

    Published 2025
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  7. 7
    The top 10 putative metabolites with the largest negative fold change between normal and thin cows.

    The top 10 putative metabolites with the largest negative fold change between normal and thin cows. by Bhuripit Saraphol (20855573)

    Published 2025
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  8. 8
    The top 10 putative metabolites with the largest positive fold change between normal and thin cows.

    The top 10 putative metabolites with the largest positive fold change between normal and thin cows. by Bhuripit Saraphol (20855573)

    Published 2025
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  9. 9
    Linear discriminant analysis (LDA) for effect size (LEfSe) analysis of endophytic bacterial communities associated with <i>Synsepalum dulcificum</i> among the three phenotypic groups.

    Linear discriminant analysis (LDA) for effect size (LEfSe) analysis of endophytic bacterial communities associated with <i>Synsepalum dulcificum</i> among the three phenotypic grou... by Rabiath F. R. Adigoun (14189090)

    Published 2025
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  10. 10
    Selection frequencies plotted in decreasing order.

    Selection frequencies plotted in decreasing order. by Hannah P. Rowe (7830599)

    Published 2025
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  11. 11
    Performance comparison of machine learning models across accuracy, AUC, MCC, and F1 score on GMSC dataset.

    Performance comparison of machine learning models across accuracy, AUC, MCC, and F1 score on GMSC dataset. by Seongil Han (20490232)

    Published 2024
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  12. 12
    Performance enhancements of classification models using the undersampling method (NearMiss).

    Performance enhancements of classification models using the undersampling method (NearMiss). by Seongil Han (20490232)

    Published 2024
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  13. 13
    Features on GMSC dataset used in NATE.

    Features on GMSC dataset used in NATE. by Seongil Han (20490232)

    Published 2024
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  14. 14
    Performance comparison of AUC, MCC, and F1 across different IR on GMSC dataset.

    Performance comparison of AUC, MCC, and F1 across different IR on GMSC dataset. by Seongil Han (20490232)

    Published 2024
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  15. 15
    Searching space for hyperparameters in Table 7.

    Searching space for hyperparameters in Table 7. by Seongil Han (20490232)

    Published 2024
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  16. 16
    GMSC dataset (IR: Imbalance Ratio).

    GMSC dataset (IR: Imbalance Ratio). by Seongil Han (20490232)

    Published 2024
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  17. 17
    Evaluation of benchmark and optimal model performance with resampling techniques.

    Evaluation of benchmark and optimal model performance with resampling techniques. by Seongil Han (20490232)

    Published 2024
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  18. 18
    Local and global interpretability of NATE.

    Local and global interpretability of NATE. by Seongil Han (20490232)

    Published 2024
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  19. 19
    Under-sampled dataset.

    Under-sampled dataset. by Seongil Han (20490232)

    Published 2024
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  20. 20
    The system architecture of NATE.

    The system architecture of NATE. by Seongil Han (20490232)

    Published 2024
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