يعرض 1 - 20 نتائج من 4,058 نتيجة بحث عن '(( element data algorithm ) OR ((( selected using algorithm ) OR ( neural coding algorithm ))))', وقت الاستعلام: 0.69s تنقيح النتائج
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    Data and code resources. حسب Sam Hall-McMaster (10343795)

    منشور في 2025
    الموضوعات:
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    Features selection using the Boruta algorithm. حسب Nishat Tasnim Thity (21755858)

    منشور في 2025
    "…We identified the important features related to IA using the Boruta algorithm. Predictions were made using different machine learning (ML) (decision tree (DT), random forest (RF), support vector machines (SVMs), and logistic regression (LR)) models. …"
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    Feature selection using Boruta algorithm. حسب Shayla Naznin (13014015)

    منشور في 2025
    "…Feature selection was performed using the Boruta algorithm and model performance was evaluated by comparing accuracy, precision, recall, F1 score, MCC, Cohen’s Kappa and AUROC.…"
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    Feature selection using the Boruta algorithm. حسب Guang Tu (22054865)

    منشور في 2025
    "…We extracted baseline characteristics, laboratory parameters, and clinical outcomes. The Boruta algorithm was employed for feature selection to identify variables significantly associated with in-hospital mortality, and 16 machine learning models, including logistic regression, random forest, gradient boosting, and neural networks, were developed and compared using receiver operating characteristic (ROC) curves and area under the curve (AUC) analysis. …"
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    Variable selection procedure using the Boruta algorithm. حسب Qiudie Liu (22655907)

    منشور في 2025
    "…<p>Variable selection procedure using the Boruta algorithm.</p>…"
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    GA pseudo-code. حسب Jianpeng Zhang (528185)

    منشور في 2025
    "…GA is used to optimize the feature selection process to identify the key feature subsets that have the greatest impact on model performance. …"
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    List of the time used by each algorithm. حسب JiaMing Gong (20427837)

    منشور في 2024
    "…In this manner high quality and common samples are randomly selected for training the classifier. Finally, to solve the issue of concept drift, EDAC designs and implements an ensemble classifier that uses a self-feedback strategy to determine the initial weight of the classifier by adjusting the weight of the sub-classifier according to the performance on the arrived data chunks. …"
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