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processing selection » process reflection (Expand Search)
robust optimization » process optimization (Expand Search), robust estimation (Expand Search), joint optimization (Expand Search)
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Showing 21 - 40 results of 95 for search '(( binary most robust optimization algorithm ) OR ( binary data processing selection algorithm ))', query time: 0.62s Refine Results
  1. 21
    COFFO control parameters.

    COFFO control parameters. by Nebojsa Bacanin (13932723)

    Published 2022
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  2. 22
    Convergence graphs for ten CEC 2019 benchmark functions and direct comparison between COFFO and FFO.

    Convergence graphs for ten CEC 2019 benchmark functions and direct comparison between COFFO and FFO. by Nebojsa Bacanin (13932723)

    Published 2022
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  3. 23
    <i>hi</i>PRS algorithm process flow.

    <i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

    Published 2023
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  4. 24
    The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC12.

    The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC12. by Aninda Astuti (21736334)

    Published 2025
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  5. 25
    The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC11.

    The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC11. by Aninda Astuti (21736334)

    Published 2025
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  6. 26
    The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC10.

    The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC10. by Aninda Astuti (21736334)

    Published 2025
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  7. 27
    The maximum accuracy (lowest error rate) with the least number of ANOVA-ranked genes achieved by different feature filtering methods and classification algorithms refined by SIRRFE under various classification tasks, including: the multiclass classification for distinguishing each individual PAH patient group (Control <i>vs</i>. IPAH <i>vs</i>. HPAH <i>vs</i>. UMC), binary classification of the healthy control <i>vs</i>. the combination of HPAH and IPAH, and the binary classification of HPAH <i>vs</i>. UMC.

    The maximum accuracy (lowest error rate) with the least number of ANOVA-ranked genes achieved by different feature filtering methods and classification algorithms refined by SIRRFE... by Song Cui (553627)

    Published 2019
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  8. 28
    List of selected mRNAs in the feature selection step.

    List of selected mRNAs in the feature selection step. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

    Published 2023
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  9. 29
    Binary sentiment polarity distribution of tweets.

    Binary sentiment polarity distribution of tweets. by Mohamed Kentour (11785652)

    Published 2021
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  10. 30
    An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA.

    An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA. by Olaide N. Oyelade (14047002)

    Published 2023
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  11. 31
    Flowchart of the proposed Margin Weighted Robust Discriminant Score (MW-RDS) algorithm.

    Flowchart of the proposed Margin Weighted Robust Discriminant Score (MW-RDS) algorithm. by Sheema Gul (21519289)

    Published 2025
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  12. 32
    The performance of classifiers based on 60 selected mRNAs.

    The performance of classifiers based on 60 selected mRNAs. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

    Published 2023
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  13. 33
    Performance of the ceRNAR package using synthetic data with randomly selected correlation values from 0.8 to 0.9.

    Performance of the ceRNAR package using synthetic data with randomly selected correlation values from 0.8 to 0.9. by Yi-Wen Hsiao (13767916)

    Published 2022
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  14. 34
    Selected mRNAs based on association rule mining analysis, for early and late stages of PTC.

    Selected mRNAs based on association rule mining analysis, for early and late stages of PTC. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

    Published 2023
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  15. 35
    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.9.

    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.9. by Yi-Wen Hsiao (13767916)

    Published 2022
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  16. 36
    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.5 to 0.7.

    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.5 to 0.7. by Yi-Wen Hsiao (13767916)

    Published 2022
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  17. 37
    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.4.

    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.4. by Yi-Wen Hsiao (13767916)

    Published 2022
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  18. 38
    The performance of the SVM classifier based on 60 (30 plus 30-zeroed) selected mRNAs for GSE 60542 dataset.

    The performance of the SVM classifier based on 60 (30 plus 30-zeroed) selected mRNAs for GSE 60542 dataset. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

    Published 2023
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  19. 39
    Medium-scale dataset comparative analysis using the number of features selected.

    Medium-scale dataset comparative analysis using the number of features selected. by Olaide N. Oyelade (14047002)

    Published 2023
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  20. 40
    Large-scale dataset comparative analysis using the number of features selected.

    Large-scale dataset comparative analysis using the number of features selected. by Olaide N. Oyelade (14047002)

    Published 2023
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