Search alternatives:
function optimization » reaction optimization (Expand Search), formulation optimization (Expand Search), generation optimization (Expand Search)
selection algorithm » detection algorithm (Expand Search), detection algorithms (Expand Search)
object selection » subject selection (Expand Search), object detection (Expand Search), object detector (Expand Search)
image function » immune function (Expand Search), image fusion (Expand Search), rate function (Expand Search)
function optimization » reaction optimization (Expand Search), formulation optimization (Expand Search), generation optimization (Expand Search)
selection algorithm » detection algorithm (Expand Search), detection algorithms (Expand Search)
object selection » subject selection (Expand Search), object detection (Expand Search), object detector (Expand Search)
image function » immune function (Expand Search), image fusion (Expand Search), rate function (Expand Search)
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Table 1_A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing...
Published 2025“…</p>Conclusion<p>Machine learning algorithms show promise for the detection of current frailty status, particularly in binary classification. …”
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Pseudocode of NSGAII [23].
Published 2023“…Next, to apply feature selection, a t-test and binary Non-Dominated Sorting Genetic Algorithm II (NSGAII) were chosen to be employed. …”
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Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods
Published 2022“…Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors. …”
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Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield...
Published 2022“…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”
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Seed mix selection model
Published 2022“…</p> <p> </p> <p>We applied the seed mix selection model using a binary genetic algorithm to select seed mixes (R package ‘GA’; Scrucca 2013; Scrucca 2017). …”
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High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: A more precise and personalized gliomas management
Published 2020“…Feature reduction was performed by ANOVA+ Mann-Whiney, spearman correlation analysis, least absolute shrinkage and selection operator (LASSO) and Gradient descent algorithm (GBDT). …”
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GSE96058 information.
Published 2024“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). …”
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The performance of classifiers.
Published 2024“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). …”
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PathOlOgics_RBCs Python Scripts.zip
Published 2023“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”
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Min-Cut/Max-Flow Problem Instances for Benchmarking
Published 2022“…<div><div>Min-Cut/Max-Flow Problem Instances for Benchmarking </div><div><p>This is a collection of min-cut/max-flow problem instances that can be used for benchmarking min-cut/max-flow algorithms. The collection is released in companionship with the paper:</p> </div><div><ul><li>Jensen et al., “Review of Serial and Parallel Min-Cut/Max-Flow Algorithms for Computer Vision”.…”
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